Evaluation of Different Simulation Methods for Analyzing Flood

applied sciences

Article Evaluation of Diﬀerent Simulation Methods for Analyzing Flood Scenarios in the Danube Delta

Alexandru Banescu 1,2 , Maxim Arseni 1,* , Lucian Puiu Georgescu 1, Eugen Rusu 3 and Catalina Iticescu 1

1 Faculty of Science and Environment, “Dunărea de Jos” University of Galati, European Center of Excellence for the Environment, 47 Domneasca Street, 800008 Galati, Romania; [email protected] (A.B.); [email protected] (L.P.G.); [email protected] (C.I.) 2 Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112 Tulcea, Romania 3 Faculty of Engineering, “Dunărea de Jos” University of Galati, 47 Domneasca Street, 800008 Galati, Romania; [email protected] * Correspondence: [email protected]; Tel.: +40-746-407-084

Received: 16 October 2020; Accepted: 22 November 2020; Published: 24 November 2020

Abstract: The present work is focused on the analysis of flood scenarios for the settlements near the Danube discharge area into the Black Sea. From this perspective, the aim of the research is the development of flood extension maps for localities in the Danube Delta. The emphasis is on collecting the data and information needed for the entire analysis process, such as hydrological data on Danube flows and water levels (which were analyzed for 51 years), topo-bathymetric data (where 1685 cross sections were processed, measured on an 87-km section of the Danube), a digital terrain model (DTM), and others. Two methods of flood scenario analysis for the localities targeted were used in this paper. The first method was an analysis of the flood scenarios by modeling a real scenario, where it was supposed that a 20 m breach appeared in the dam which protects the localities and remained present for 24 h. The second method consisted of a Geographic Information System (GIS) analysis (static from a hydraulic point of view), where the maximum water level was superimposed over the DTM. This corresponded to a scenario in which the breach in the flood-control levee remains present for a longer period. The validated results show that the dynamic method is more efficient than the static method, both in terms of estimated flooded surfaces and in terms of simulation accuracy (taking into account more input parameters than the static method). Thus, from the obtained simulations it was observed that applying the dynamic method resulted in smaller flooded surfaces in the settlements analyzed than when considering the static method. In some cases, the differences between the flooded surfaces reached up to about 22%. This information is important and of general interest since it can be used in various fields of work, such as flood defense strategies, and investment promotion activities in the Danube discharge area or similar locations.

Keywords: hydraulic modeling; GIS processing; ﬂooding maps; Danube Delta

1. Introduction Although, extremely important for humanity in many respects, water also carries a significant destructive potential, causing damage to people through the occurrence of natural hazards such as floods [1]. Floods represent the most common hydrological hazard in Romania, as well as in many places all over the world [2]. Over time, floods have occurred in various regions of the country, causing particularly significant damage, both in terms of infrastructure, and in terms of households and agriculture [3].

Appl. Sci. 2020, 10, 8327; doi:10.3390/app10238327 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 8327 2 of 28

Floods are natural phenomena that cannot be prevented. Nevertheless, some human activities (such as the increasing number of human settlements and economic assets in floodplains, as well as the reduction of natural capacities for water retention through certain land uses) together with climate change, contribute to the increased likelihood of floods and their negative impact. The study of these phenomena in the Danube Delta is important because recent flood events have increased in number and magnitude in this area, and many settlements have been affected. This river flooding triggers significant level and flow variations along the river, endangering infrastructure elements (mainly dykes of settlements), agricultural areas, and households [4]. Beltaos [5] used the Hydrologic Engineering Center’s river analysis system (HEC-RAS) to evaluate spring breakup of the ice cover which is the main agent of flooding and replenishment of the Peace–Athabasca Delta. This study showed that different ice-jam scenarios indicated that jams of moderate, rather than extreme, length were more effective in flooding the Delta. In addition, Nones [6] used numerical modeling of the hydro-morphodynamics of a distributary channel of the Po River Delta (Italy) during the Spring 2009 flood event to show how the dimensions of the river plume can influence the evolution of the prodelta, while having a rather negligible effect inland, because of the major stresses induced by the high river discharge during the flood event. Another interesting study was carried out by Win [7] who evaluated the flooding of vulnerable areas by a different return period flood, by using the Hydrologic Engineering Center’s river analysis system (HEC-RAS) model in the Ayeyarwady Delta Region, Myanmar. The results of this study showed that the arable land and forest were the most vulnerable to different return period flooding. The HEC-RAS software represents an affordable method for this type of assessment. According to Molina [8], the acquisition, management, and use of spatial data are essential for quality of water resource studies. Thus, several geomatic methods serve hydrological modeling, and emphasizing the generation of cartographic products. Furthermore, a study developed by Zazo [9] on the analysis of flood modeling through innovative geomatic methods, aimed to evaluate the flood risk, while assessing the suitability of an innovative technique. The results of this study showed that the developed methodology and technology allowed for a more accurate riverbed representation. Gergel’ová [10] evaluated the selected sub-elements of spatial data quality on 3D flood event modeling. The study focused on the evaluation of the state of spatial information quality. The results of the study show that the combination of different type of the input data, like a digital relief model combined with geodetic or topographical data, can provide a redefined 3D model. This type of input data influences directly the hydrodynamic modeling process. In the Danube Delta, floods are hydrological events that can occur at any scale. These phenomena have appeared more and more often in the last decades. Due to the geographical position, and the specifics of the area, as well as the unique natural character of the Danube Delta, hydrological hazards (heavy showers, sometimes accompanied by hail, river flooding, strong storms, extreme temperatures, etc.) frequently arise [11]. The flooding process is influenced by the rate of the Danube water level rise. Depending on their magnitude, they proportionally affect a certain part of the Delta surface, and especially the settlements along the main branches of the river [12]. The Danube Delta is a very dynamic area, whose main direction of evolution is horizontal and vertical growth. The character of the water level variations in the Danube Delta presents a series of particularities specific to the discharge area of the Danube into the Black Sea. Of course, the variation of the water levels in the Delta is, first of all, closely related to the variation of the water supply at the entry point at the Chilia Ceatal, and to the variation in the wind where the water flows into the Black Sea [13,14]. General practice has shown that floods cannot be prevented. Still, they can be managed, and their effects can be reduced through a systematic process that leads to a series of measures and actions designed to help reduce the hazards associated with these phenomena [15]. The capacity of flood reservoirs is influenced by the scale of each type of river flooding. Similar river flooding, in terms of maximum water level reached and duration, can find the inside area of the delta in different stages of Appl. Sci. 2020, 10, 8327 3 of 28 initial accumulation, which contributes to emphasizing the complexity of the flooding phenomenon. Therefore, due to the varied relief and the particularities of the flow regime of each flood, the flooding process across the entire delta becomes particularly complex [16]. Floods in the Danube Delta become natural disasters when they affect human settlements. The vast majority of localities in the Danube Delta are situated in floodable areas, especially those distributed along the main branches of the Danube, presenting a high hazard of flooding [17]. The hazard is determined by the failure of the dams to defend the localities, due to severe river flooding on the Danube and its extended duration. The pressure of the flood exerted on the dyke causes it to give in, especially in vulnerable areas (a breach/rupture is created in a section of the dam), endangering homes and households [18]. For a wetland with a variety of ecosystems and biodiversity, such as the Danube Delta, one of the most important conditions is the water circulation system [19]. Different land use modifications over time of some important territories, by disregarding the effect of the floods, have caused a decrease of the storage capacity of the Delta by approximately 34%, and at the same time, modified the environmental and social characteristics [20]. This area is of great interest for flood analysis because the settlements in the Danube Delta have a long history of destructive events. A good amount of Danube water is taken by the main branches and distributed to the mouths at the Black Sea [21]. In the Danube Delta there is a danger of spillage for dammed areas in places where the dam tamping phenomena (dam falling) has occurred, and even the major danger of infiltration due to the very long duration of river flooding on the Danube [22]. At the same time, there is the permanent danger of erosion, sometimes accompanied by landslides, that endanger homes and households. Identifying the scenarios in which river flooding can occur on the Danube is central in the broader flood scenarios analysis. Once the context of the specific flood problem to be addressed has been established, the hazard of flooding must be identified and analyzed [23]. This involves hydrological and hydraulic analyses using specialized software that models flood phenomena. [24] Some of the key elements resulting from the flood scenarios analysis are flood extension maps, as well as tables and graphs showing the state of play during the unfolding of these phenomena [25]. The first problem that arises when elaborating flood maps is the choice of hydrological situation when the water levels on the main branches are high, and this must be considered and analyzed [26]. Therefore, the main objective of this study was the evaluation of two simulation methods to assess extent of flooding for the settlements in the Danube Delta, by using low-cost equipment and less time-consuming simulation methods, to obtain flood maps corresponding with different flood scenarios. The flood extension maps depict the potentially flooded areas of the settlements, as a result of the occurrence of a breach (rupture) in the dyke. Furthermore, a comparison was made between the results obtained using two methods that analyze different scenarios of breaching the dyke. The analysis will be applied to the settlements on the course of the Sulina branch, located near the area where the Danube discharges into the Black Sea.

2. Study Area and Data

2.1. Study Area The Danube Delta is located in the eastern extremity of Romania and covers 2.5% of the country’s area. The Danube Delta, including the peripheral lake areas, extends North to 45◦270 northern latitude (Chilia branch, 43 km), South to 44◦200 (Cape Midia), West to 28◦100 E (Cat’s Bend) and East to 29◦420 E (Sulina) [27]. The Danube Delta is a complex environment, governed by the interactions between the Danube River and the Black Sea [28]. Ecosystem services, provided by the Danube Delta, are very diverse, due to the heterogeneity and high dynamism of ecological systems in the area [29]. As a result of the decrease of the ﬂoodplains in the Danube Delta area, the nutrient retention capacity has decreased, so the Danube waters are aﬀected by strong eutrophication. These phenomena Appl. Sci. 2020, 10, x FOR PEER REVIEW 4 of 28

flood phenomena. The transformation of wetlands into terrestrial ecosystems and the modification of ecosystem structures have decreased the capacities of floodable surfaces, as well as ecological, economic, recreational, aesthetic, and educational capacities. In the present study, the settlements located along one of the three main branches of the Danube Appl. Sci. 2020, 10, 8327 4 of 28 (the Sulina branch with a length of approximately 71 km), which partly ensure the water flow to the Black Sea, were taken into account (Figure 1). The settlements analyzed were Partizani, Ilganii de Sus, aVulturu,ffect trophic Maliuc, cycles, Gorgova, leading Mila to a 23, decrease and Cri inșan biologicaland Sulina. diversity These throughlocalities theare disappearancevulnerable to the of somehigh- species,water level many of the of which Danube. have high landscape and even economic value [30]. The Delta area requires majorThese investment waters, andentering the developmentthe delta, reach of the quality sea following studies on several the modeling flow paths of; floodthe Chilia phenomena. branch, Thefollowed transformation by the branches of wetlands of the into secondary terrestrial Chilia ecosystems delta and and the the Tulcea modification branch, of splitting ecosystem onwards structures into havethe Sulina decreased and Sfantu the capacities Gheorghe of branches. floodable Between surfaces, the as main well water as ecological, flow routes economic, through recreational,the Danube aesthetic,Delta and and the educationalinterior of the capacities. delta, and between these routes and the areas outside the delta, as well as betweenIn the present the delta study, and thethe settlementssea and inside located the alongdelta, onethere of are the sev threeeral main connecting branches routes of the for Danube water (thecirculation, Sulina branchwhich withplay a lengthparticular of approximately role in the hydrological 71 km), which regime partly of the ensure delta the [31] water. Through flow tothese the Blackroutes, Sea, a natural were takenaccumulation into account reservoir (Figure role1). is Theplayed settlements by the interior analyzed of the were delta Partizani, and by some Ilganii outer de

Sus,areas, Vulturu, and secondary Maliuc, Gorgova,routes of Milawater 23, flow and to Cri thes, anand sea appear. Sulina. The These regime localities of this are natural vulnerable reservoir to the is high-waterdetermined level by the of flow the Danube. of the Danube at the starting point of the Delta [32].

FigureFigure 1.1. Geographical locations of the settlements along the Sulina branch considered for this study study,, ((aa)) the the study study area area superimposed superimposed on the on topographic the topographic map; ( b map;) the geographical (b) the geographical position of position the settlements of the superimposedsettlements superimposed on the ortho-photomaps on the ortho of- thephotomaps Danube Delta of the (the Danube green dots Delta are (the the greensettlements dots locatedare the alongsettlements the Sulina located branch) along andthe Sulina the patch branch) on which and the topo-bathymetric patch on which topo measurements-bathymetric were measurements performed (S1, S2 describe the cross sections measured in the first part of the river, and S3 describes the cross section were performed (S1, S2 describe the cross sections measured in the first part of the river, and S3 measured in the river’s second part); (c) the study area superimposed on the digital terrain model. describes the cross section measured in the river’s second part); (c) the study area superimposed on the digital terrain model. These waters, entering the delta, reach the sea following several flow paths; the Chilia branch, followed by the branches of the secondary Chilia delta and the Tulcea branch, splitting onwards into A major flood event in the Danube Delta was identified in July 2010 and examined in the present the Sulina and Sfantu Gheorghe branches. Between the main water flow routes through the Danube study. Major floods were generated by the appearance of significant water flow on the Danube. The Delta and the interior of the delta, and between these routes and the areas outside the delta, as well flow resulting from the river flooding was of 16,600 m3/s, as opposed to the 6658 m3/s mean flow as between the delta and the sea and inside the delta, there are several connecting routes for water (between 1965–2015), registered at the Tulcea-port hydrometric station. The maximum level of the circulation, which play a particular role in the hydrological regime of the delta [31]. Through these flood was recorded on 6 July 2010 as 4.95 m, measured for the Black Sea Sulina system, opposed to routes, a natural accumulation reservoir role is played by the interior of the delta and by some outer the 2.50 m mean level. A flow hydrograph with a peak discharge of 16,600 m3/s was created and areas, and secondary routes of water flow to the sea appear. The regime of this natural reservoir is validated in the present study for the analysis of flood scenarios for settlements distributed along the determined by the flow of the Danube at the starting point of the Delta [32]. Sulina branch [33]. A major flood event in the Danube Delta was identified in July 2010 and examined in the present study. Major floods were generated by the appearance of significant water flow on the Danube. The flow resulting from the river flooding was of 16,600 m3/s, as opposed to the 6658 m3/s mean flow (between 1965–2015), registered at the Tulcea-port hydrometric station. The maximum level of the flood was recorded on 6 July 2010 as 4.95 m, measured for the Black Sea Sulina system, opposed to the Appl. Sci. 2020, 10, 8327 5 of 28

2.50 m mean level. A ﬂow hydrograph with a peak discharge of 16,600 m3/s was created and validated in the present study for the analysis of ﬂood scenarios for settlements distributed along the Sulina Appl. Sci. 2020, 10, x FOR PEER REVIEW 5 of 28 branch [33].

2.2.2.2. Data Data MostMost of of the the time, time, floods floods occur occur as aas result a result of high-waterof high-water levels levels that that are are characterized characterized by reachingby reaching a maximuma maximum point point in terms in terms of flow of value.flow value. Thus, Thus, the available the available hydrological hydrological data for data 51 years,for 51 betweenyears, between 1965 and1965 2015, and were 2015, studied. were studied. Thus,Thus, Figure Figure2 presents 2 presents the the variation variation of theof the average average annual annual flows flows at theat the Danube Danube Delta Delta entrance entrance point,point, distributed distributed by by years, years, throughout throughout the the analysis analysis period. period.

1212000,000 Q annual average 1965-2015 (m3/s) qq Level Q max. an. av. (m3/sec) 1010000,000

8000 /s) 3 6000 (m Q

4000

2000

0 J 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year Figure 2. Variation of the average annual flows at the entrance to the Danube Delta, between 1965 Figure 2. Variation of the average annual flows at the entrance to the Danube Delta, between 1965 and 2015. and 2015. The average flows of the Danube were then calculated at intervals of 5, 6, and 10 years, respectively, both atThe the averagedelta entry flows point of andthe Danube on the three were main then branches calculated (1965–1970, at intervals 1971–1980, of 5, 6, and 1981–1990, 10 years, 1991–2000,respectively, 2001–2010, both at 2011–2015) the delta entry (Table point1). and on the three main branches (1965–1970, 1971–1980, 1981–1990, 1991–2000, 2001–2010, 2011–2015) (Table 1). Table 1. Average flows of the Danube at intervals of 5, 6, and 10 years, respectively, at the entrance to theTable Delta 1. andAverage on the flows three mainof the branches Danube (betweenat intervals 1965–2015). of 5, 6, and 10 years, respectively, at the entrance to the Delta and on the three main branches (between 1965–2015). The Watercourse The Watercourse Period Danube Entry Sulina Branch Sfantu Gheorghe Branch Chilia Branch Period Danube Entry Sulina Branch Sfantu Gheorghe Branch Chilia Branch 3 3 3 3 Q * (m Q/s) * (m3/s)% %Q (mQ (m/s)3/s) %% QQ (m (m/s)3/s) %% QQ (m (m3/s)/s) %% 1965–19701965–19707683 7683 100100 13011301 16.9316.93 15221522 19.8119.81 4674 4674 60.84 60.84 1971–19801971–19806892 6892 100100 12891289 18.7018.70 15101510 21.9121.91 4076 4076 59.14 59.14 1981–19901981–19906209 6209 100100 12351235 19.8919.89 13991399 22.5322.53 3606 3606 58.08 58.08 1991–20001991–20006240 6240 100100 12531253 20.0820.08 15831583 25.3725.37 3390 3390 54.33 54.33 2001–2010 7005 100 1407 20.09 1572 22.44 4379 62.51 2001–2010 7005 100 1407 20.09 1572 22.44 4379 62.51 2011–2015 6250 100 1255 20.08 1586 25.38 3396 54.34 2011–2015 6250 100 1255 20.08 1586 25.38 3396 54.34 * Q—river discharge. * Q—river discharge. From the analysis of this data, it could be concluded that the most probable value of the average flow overFrom the the entire analysis analysis of this period data, at it the could entry be point concluded into the that delta the is most between probable the approximate value of the limitsaverage offlow 6700–6750 over the m3 entire/s. The analysis data analysis period also at the shows entry that point the into maximum the delta annual is between peak flowthe approximate was recorded limits in 3 2010,of 6700 a value–6750 of 16,600m /s. The m3 /datas, followed analysis by also 1970. shows This canthat be the explained maximum by annual taking into peak account flow was variations recorded 3 inin average 2010, annual a value flows of 16,600 over time. m /s, followed by 1970. This can be explained by taking into account variationsThe flow in that average entered annual from flows the branchesover time. of the Danube into the main units of the delta in the periodThe 1965–2015 flow that was entered reduced. from This the reduction branches wasof the caused Danube by into the alluvialthe main clogging units of of the some delta canal in the sleepersperiod and 1965 the–2015 closure was of reduced. others that This generated reduction clogging was caused in the by adjacent the alluvial spaces clogging [34]. From of thesome longer canal sleepers and the closure of others that generated clogging in the adjacent spaces [34]. From the longer data series, starting with 1996, it was found that the years in which average annual flows of less than 6000 m3/s were recorded were the years 2003, 2007, 2008, 2011, and 2012, and average annual flows of more than 8000 m3/s were recorded in the years 2005, 2006, and 2010. River flooding usually occurs during the high-water phase [35]. Appl. Sci. 2020, 10, 8327 6 of 28 data series, starting with 1996, it was found that the years in which average annual flows of less than 6000 m3/s were recorded were the years 2003, 2007, 2008, 2011, and 2012, and average annual flows of Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 28 more than 8000 m3/s were recorded in the years 2005, 2006, and 2010. River flooding usually occurs duringThe the high high-water-water phaselevels of [35 the]. 2005, 2006, and 2010 floods were imposed by the restriction of the freedomThe high-water space of the levels lower of Danube the 2005, by 2006, damming and 2010 the floodsmeadow, were combined imposed with by the significant restriction rainfall. of the freedomIn spaceaddition, of the in lowerJuly 2010, Danube large by-scale damming floods theoccurred meadow, in the combined Danube with Delta significant as a result rainfall. of a strong riverIn flooding addition, on in the July Danube. 2010, large-scale This phenomenon floods occurred caused inthe the almost Danube complete Delta asflooding a result of of settlements a strong riverprotected flooding with on local the Danube. works or This those phenomenon simply unprotected, caused the but almost also the complete rupture flooding of some of dams settlements that had protectedbeen dimensioned with local worksaccording or those to a class simply of greater unprotected, importa butnce also (such the as rupture hydro of-technical some dams works). that At had the beensame dimensioned time, the fact according that there to was a class an ofaccumulation greater importance of water (such coming as hydro-technicalalmost simultaneously works). from At the the sameflood time, in the the whole fact that delta, there combined was an accumulationwith the fact that of water the discharge coming almost to the simultaneouslysea was cumbersome, from the led floodto prolonged in the whole periods delta, of combined water pressure with the on factthe thatprotective the discharge dams. to the sea was cumbersome, led to prolongedThe periodsyear 2010 of represent water pressureed the onreference the protective year for dams. the present study, considering the floods that tookThe place year in 2010 the Danube represented Delta. the Figure reference 3 shows year forthe themonthly present situation study, consideringof water levels the for floods 2010 that and took2003. place Levels in the were Danube recorded Delta. at the Figure Tulcea3 shows-port hydrometric the monthly situationstation, located of water upstream levels for of 2010the studied and 2003.area. Levels The data were analysis recorded for at2003 the and Tulcea-port 2010 was hydrometric based on a series station, of daily located data upstream (daily recordings) of the studied of the area.Danube The datawater analysis levels. forFrom 2003 the and analysis 2010 wasof the based data onpresented a series ofin dailyFigure datas 2 and (daily 3, it recordings) was found of that the in Danube2010, the water water levels. level From of the the Danube analysis reached of the datathe maximum presented value in Figures of 4.952 and m 3(regarding, it was found the Black that in Sea 2010,at Sulina). the water level of the Danube reached the maximum value of 4.95 m (regarding the Black Sea at Sulina).

Figure 3. Monthly evolution of the Danube water levels at the Tulcea-port hydrometric station during Figure 3. Monthly evolution of the Danube water levels at the Tulcea-port hydrometric station during 2003 and 2010. 2003 and 2010. In 2003, the historical minimum water level of the last 20 years was registered in September, due toIn the 2003, warm the period historical of the minimum year and water low rainfall. level of This the last minimum 20 years generally was registered appears in most September, frequently due into October, the warm but period also in of the the ﬁrst year and and second low rainfall. half of This September. minimum The generally historical appears minimum most waterfrequently level in reachedOctober, the but value also of in 0.61 the m first (with and reference second to half the ofBlack September. Sea at Sulina) The historical and represented minimum a dry water year levelfor thereached Danube the Delta. value Figureof 0.61 mA1 (with (Appendix referenceA) showsto the Black the monthly Sea at Sulina) distribution and represented of the minimum a dry year and for maximumthe Danube water Delta. levels Figure at the A1 Tulcea-port (Appendix hydrometric A) shows stationthe monthly in 2003 distribution and 2010. of the minimum and maximumThe water water ﬂow le registeredvels at the onTulcea the Danube-port hydrometric in the summer station of 2010,in 2003 corresponding and 2010. to the maximum level ofThe 4.95 water m with flow reference registered to on the the Black Danube Sea Sulina, in the summer reached of a peak2010, ofcorresponding approximately to the 16,600 maximum m3/s, 3 atlevel the Tulcea-port of 4.95 m with hydrometric reference station. to the Black Sea Sulina, reached a peak of approximately 16,600 m /s, at the Tulcea-port hydrometric station.

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3. Materials and Methods The present study comprises the analysis of two scenarios where ruptures occur in the settlements’ dykes in conditions of maximum levels on the Danube. The difference between these two scenarios is how long the breach remains open in the dyke. Thus, the first scenario of flood analysis was applied to theAppl. settlements Sci. 2020, 10, byx FOR modeling PEER REVIEW a real scenario with the occurrence of a 20 m opening (breach) in the7 dyke,of 28 remaining present for 24 h at the maximum water levels of the Danube. The second scenario of flood The second scenario of flood analysis was applied to the settlements through a Geographic analysis was applied to the settlements through a Geographic Information Systems (GIS) analysis Information Systems (GIS) analysis (static from a hydraulic point of view), consisting of (static from a hydraulic point of view), consisting of superimposing the information corresponding superimposing the information corresponding to the maximum water level over the digital terrain tomodel. the maximum It corresponds water level to the over most the unfavorable digital terrain scenario, model. namely It corresponds the breach/breaches to the most unfavorable remaining scenario,present in namely the dyke the at breach the same/breaches time as remaining the maximum present water in the levels dyke of at the the Danube same time for asa longer the maximum period water(more levels than one of the day). Danube for a longer period (more than one day). ForFor the the analysis analysis of of the the dam dam breaching breaching scenarios, scenarios, two two methods methods were were used: used: the the dynamic dynamic method method for thefor first the scenario, first scenario and the, and static the method static for method the second for the scenario. second The scenario. steps followed The steps are summarized followed are in asummarized process diagram in a process (Figure diagram4). (Figure 4).

FigureFigure 4.4. DiagramDiagram indicatingindicating the methodology considered in in the the present present work work,, and and which which describes describes thethe main main steps.steps.

AccordingAccording to to the the figure figure above, above, the diagram the diagram of the ofapplied the applied process involvesprocess involves a continuous, a continuous, interactive, andinteractive, dynamic and process. dynamic These process. stages These do not stages form ado linear not form process a linear but move process sequentially but move fromsequentially one step tofrom another one step and to vary another depending and vary on depending the results ofon each the results stage. of It iseach clear stage. from It theis clear scheme from that the thescheme input datathat arethe veryinput important data are very in flood important scenario in flood analysis. scenario analysis. TheThe datadata collectioncollection processprocess begins with a regio regionalnal assessment, an an exploratory exploratory phase, phase, or or even even an an investigation,investigation, which which provide provide input input for for the the proposed proposed diagram diagram [36 [36]]. Following. Following the the data data collection, collection, in in the firstthe phasefirst phase the raw the dataraw data are obtained are obtained (in this (in case,this case, the hydrological the hydrological data, data, water water flows flows and water and water levels, andlevels topo-bathymetric, and topo-bathymetric data, transverse data, transverse profiles, profiles, topographic topographic maps, maps, the digital the digital terrain terrain model, model, ortho photomaps,ortho photomaps, satellite satellite images, images, etc.), representing etc.), representing the primary the dataprimary that datarequire that elementary require elementary processing forprocessing further usefor [further37]. The use data [37] are. The obtained data are from obtained external from sources, external such sources, as administrative such as administrative flow data, existingflow data, studies existing and studies research, and maps, research, photographs, maps, photog or internalraphs, sourcesor internal (collected sources during (collected the studyduring using the anstudy appropriate using an methodology).appropriate methodology). The accuracy The of theaccuracy results of of the the results analysis of the depends analysis on depends the accuracy on the of theaccuracy raw data of the [38 ].raw data [38]. AsAs shownshown inin FigureFigure4 ,4, the the proposed proposed methodology methodology combinescombines twotwo methodsmethods ofof analysis.analysis. In In the the approachapproach ofof eacheach method,method, we need input data, such as as the the digital digital terrain terrain model, model, hydrological hydrological data, data, topo-bathymetrictopo-bathymetric data,data, andand topo-geographictopo-geographic data for the Danube Delta Delta area. area. The basic element that the two methods are applied to is the digital terrain model (DTM). The DTM has the role of rendering the terrain configuration continuously from a spatial point of view, and can be manipulated by computer programs. Today, light detection and ranging (LIDAR) technology is known as an important source of geospatial information. The benefits of this technology are particularly valuable in a wide range of scientific and professional fields, such as land monitoring, hydraulic modeling of rivers/streams, and more [39,40]. The data from the DTM used in this study (Figure A2) shows the terrain with a high degree of accuracy, for both axis direction: horizontally, 0.5 m × 0.5 m, and vertically, 0.05 m. The accuracy is more important than resolution in hydraulic and

Appl. Sci. 2020, 10, 8327 8 of 28

The basic element that the two methods are applied to is the digital terrain model (DTM). The DTM has the role of rendering the terrain configuration continuously from a spatial point of view, and can be manipulated by computer programs. Today, light detection and ranging (LIDAR) technology is known as an important source of geospatial information. The benefits of this technology are particularly valuable in a wide range of scientific and professional fields, such as land monitoring, hydraulic modeling of rivers/streams, and more [39,40]. The data from the DTM used in this study (Figure A2) shows the terrain with a high degree of accuracy, for both axis direction: horizontally, 0.5 m 0.5 m, × and vertically, 0.05 m. The accuracy is more important than resolution in hydraulic and hydrological modeling research and GIS analysis. The data files contain spatial terrain height data in a digital format, usually in a rectangular grid. As the study area considered overlaps with an extremely large space, the DTM had to cover the entire area of the Danube Delta itself, in order to have good data processing for the hydraulic modeling and the GIS processing. The content of the digital model of the land supports the design of the flood protection lines for the settlements, and at the same time creating a useful tool for analyzing the dynamics of hydro–geo–morphological phenomena, which are very dynamic in the study area. The correlation between these phenomena and land use can only be made on such a model. The hydro–geo–morphological units were mapped using a LIDAR based DTM [41]. This DTM was obtained from LIDAR measurements performed within the project “development of a high resolution digital cartographic support necessary for the implementation of plans, strategies and management schemes in the Danube Delta Biosphere Reserve”—CARTODD [42]. The high resolution digital cartographic framework developed in this project comprises several components: the DTM, the digital elevation model (DEM), elevation classes (EC), and ortho photomaps (OFM). These components represent the foundation layer for approaching the two methods in this study. They were developed by processing primary cartographic data, obtained by modern methods (data collection by LIDAR method), and the high accuracy of ortho photomaps (2.5 MP) allowed detailed visualization of the areas of interest. The development of the numerical field model (NFM) with the LIDAR method was based on the detection of the echo/retro diffusion of a laser pulse, and the measurement of the round-trip flight time of the aircraft-scanned object (e.g., a land surface). The flight parameters were chosen to achieve the required precision objectives, in altitude on the Z axis of 5 cm, and the necessary and sufficient density of points (i.e., average 4–5 pts/m2), to correctly detect the dams, taking into account the random distribution of the point cloud. The whole process of hydraulic modeling and GIS processing in flood scenarios analysis depends on the accuracy of the digital terrain model. The interpolation method for minor riverbeds used in this study was inverse distance weighting (IDW). This method is a deterministic spatial interpolation model, based on an assumption that the value at an unsampled point can be approximated by a weighted average of observed values within a circular search neighbourhood [43–45]. The DTM resolution for the minor riverbed was computed with a similar resolution as the LIDAR DTM (0.5 m 0.5 m), but it was resampled up to 1 m, which allowed × an easier input of data in the HEC-RAS and Global Mapper software. The main channel vertical accuracy was 0.10 m. In order to obtain a complex digital terrain model, the area comprising the minor riverbed of the digital model was cut and replaced with a new model of the minor riverbed updated by topo- bathymetric measurements. For the new digital model of the minor riverbed, topo-bathymetric measurements were performed in the study area (along the entire length of the Sulina branch, from Ceatal Sfantu Gheorghe to the mouth at the Black Sea) and downstream of the Sulina branch (along the entire length of the Tulcea branch, from Ceatal Chilia to Ceatal Sfantu Gheorghe), resulting in 2 measured sections. These data were collected with the help of state-of-the-art equipment provided by the Danube Delta National Institute for Research and Development. Appl. Sci. 2020, 10, 8327 9 of 28

The equipment used to perform the topo-bathymetric measurements was a SonTek ADCP RiverSurveyor M9 (SONTEK Company, San Diego, CA, USA) which used 3 3 transducers, each with × a different orientation. The topo-bathymetric measurements were performed between the 16 and 27 of March 2020, in conditions of low water levels of the Danube river, recorded at the Tulcea-port hydrometric station, namely 0.52–0.67 m. The two calibration sections in which the hydrological measurements were performed are presented in Figure1c (S1–S2 first river part and S2–S3 second river part). The following technical means were used for this activity: a motor boat, to the starboard side of which was fixed, with a special fastening system, a float on which the equipment for hydrological measurements was installed, an acoustic doppler current profiler (ADCP), equipped with sensors for measuring the water Appl. Sci. 2020, 10, x FOR PEER REVIEW 9 of 28 flow characteristic parameters, and a GPS antenna. The primary data, measured by ADCP in a cross cross-section-section through the riverbed, provide providedd info on water depth (H), the width of the the cross cross-section-section through through the the riverbed, riverbed, measured measured at at water water surface surface level, level, water flow, flow, and water flowflow velocities,velocities, measured for the entire cross section of the riverbed. Fieldwork Fieldwork for hydrological measurements is is presented in in Figure 55.. AttachingAttaching thethe floatfloat toto thethe starboardstarboard sideside ofof the boat was a necessary condition so that the ADCP sensors were positioned upstream of thethe boat’sboat's engine. Thus, the datadata measuredmeasured byby thethe sensors sensors were were not not influenced influenced by by various various parameters parameters related related to tothe the boat boat engine engine (vibrations, (vibrations, currents currents induced induced by by the the engine engine propeller, propeller, the the degree degree of attenuation of attenuation of the of waterthe water flow flow because because of the of impactthe impact with with the boat’sthe boat hull,’s hull, etc.). etc.).

FigureFigure 5. 5. TopoTopo-bathymetric-bathymetric measurements with ADCP ( (acousticacoustic d doppleroppler c currenturrent proﬁler).profiler).

A GPS antenna mounting system was available at the top of the sensor unit. The The antenna antenna collects geolocation data so that data recorded by the ADCP sensors was georeferenced. The main unit of the ADCP (console) was also installed on the same floater.floater. It pick pickeded up and connectedconnected the two signal streams (transmitted by the ADCP sensors and by the GPS antenna), and, through a radio system (transmitte(transmitter-receiver),r-receiver), communicatedcommunicated wirelessly with the on-boardon-board laptop. In this laptop, the control and data collection program from the main unit (console) waswas running.running. With this type of data collection, the bathymetric profiles profiles were based on a large series of dat dataa (cross(cross-sectional-sectional profilesprofiles were collected at average distances of of about 50 m from each other and comprisecomprisedd a large number of points for each profile).profile). The minimum distance measured between the crosscross-sectional-sectional profilesprofiles waswas 30 30 m m (in (in critical critical regions regions or or at riverat river bends), bends), and and the the maximum maximum distance distance was was70 m 70 (Figures m (Figures A3 and A3 A4 and). The A4). first The measured first measured river section river section was on was the Tulcea on the branch, Tulcea withbranch, a length with ofa lengthapproximately of approximately 17 km, having 17 km, as having a starting as pointa starting Ceatal point Chilia Ceatal (measured Chilia (measured transversal transversal profile S1), profile and as S1)stopping, and as point stopping Ceatal point Sfantu Ceatal Gheorghe Sfantu Gheorghe (measured (measured transversal transversal profile S2). profile Inside S2). this Inside river this section, river section340 transversal, 340 transversal profiles wereprofiles made were from made the leftfrom bank the toleft the bank right to bank the right of the bank river of branch, the river cumulating branch, cumulatingbetween 160 between and 250 points160 and for 250 each points measured for each profile, measured depending profile, on depending the width ofon the the branch. width of the branch. The second river section was measured on the Sulina branch (the analyzed settlements are distributed along this branch) on a length of approximately 70 km, having as a starting point Ceatal Sfantu Gheorghe, and as final point the mouth of the branch into the Black Sea (the interval between the measured cross-sectional profile S2, and the measured cross-sectional profile S3). Inside river part 2—1345 cross-sectional profiles were generated, with between 110 and 200 points for each measured profile (the width of the Sulina branch being variable along its entire length).

4. Results The calibration of the hydraulic model was first performed upstream of the study area, more precisely in the Danube entrance sector in the Delta (Ceatal Chilia), and later downstream, at the mouth of the Sulina branch in the Black Sea. The calibration of the model in the HEC-RAS software

Appl. Sci. 2020, 10, 8327 10 of 28

The second river section was measured on the Sulina branch (the analyzed settlements are distributed along this branch) on a length of approximately 70 km, having as a starting point Ceatal Sfantu Gheorghe, and as final point the mouth of the branch into the Black Sea (the interval between the measured cross-sectional profile S2, and the measured cross-sectional profile S3). Inside river part 2— 1345 cross-sectional profiles were generated, with between 110 and 200 points for each measured profile (the width of the Sulina branch being variable along its entire length).

4. Results The calibration of the hydraulic model was first performed upstream of the study area, more precisely in the Danube entrance sector in the Delta (Ceatal Chilia), and later downstream, at the mouth of the Sulina branch in the Black Sea. The calibration of the model in the HEC-RAS software was made by alternate values of the Manning roughness coefficients, between 0.025 and 0.03, and by applying the rating curve data from the Tulcea hydrometric station (Figure A5). In the next stage (unsteady flow module), in order to define the boundary conditions, an analysis of the hydrological data of the Danube was performed, which required the description of the water flow history for a period of time. Figures6 and7 show two cross-sections used for model calibration, which were made on both river parts. The absolute elevation value (Z) of the riverbed was determined by finding the difference between water surface value and a water depth value (H). The water level for the measured sections upstream and downstream of the hydrometric station was calculated using real-time kinematic positioning (RTK). To determine the water surface level, for each cross-section of the riverbed where bathymetric measurementsAppl. Sci. 20 were20, 10, x performed, FOR PEER REVIEW two methods were used to determine the elevation values.10 of 28 The first method consists of measurements with RTK Rover (ROMPOS) Spectra precision SP80, and the secondwas method made consists by alternate of readingvalues of the Manning values recorded roughness atcoefficients, the hydrometric between 0.025 stations and 0.03, (at Tulcea, and by Sulina, Sfantu-Gheorghe,applying the Chilia-Veche,rating curve data Periprava). from the Tulcea For hydrometric georeferencing station the (Figure data A5). (spatial /global referencing) In the next stage (unsteady flow module), in order to define the boundary conditions, an analysis we usedof thethe hydrological geographic data positioning of the Danube data was of performed, each point which in the require riverbedd the descrip profiletion and of the water data on the depth offlow each history point for (absolute a period of elevation time. of the riverbed, referenced to the Black Sea reference system 75-BS75). TheFigures X, Y, 6 Z and data 7 show were two thus cross georeferenced-sections used data for model obtained calibration, for each which point were measured made on byboth ADCP in the topo-bathymetricriver parts. sections on the Danube branches.

Figure 6.FigureProfile 6. Profile S1 created S1 created with with the the ADCP-profile ADCP-profile trajectory, measured measured parameters parameters (total flow, (total boat flow, boat speed, averagespeed, average speed, speed, measured measured distance, distance, depth) depth) and water water velocity velocity graph graph (velocities (velocities distributed distributed on on cells, with a colour gradient of velocity intensity, on the water column). The profile was extracted cells, with a colour gradient of velocity intensity, on the water column). The profile was extracted from from the River Surveyor Live software used in the processing of data collected by ADCP. the River Surveyor Live software used in the processing of data collected by ADCP.

Figure 7. Profile S3 created with the ADCP-profile trajectory, measured parameters (total flow, boat speed, average speed, measured distance, depth) and water velocity graph (velocities distributed on cells, with a colour gradient of velocity intensity, on the water column). The profile was extracted

Appl. Sci. 2020, 10, x FOR PEER REVIEW 10 of 28 was made by alternate values of the Manning roughness coefficients, between 0.025 and 0.03, and by applying the rating curve data from the Tulcea hydrometric station (Figure A5). In the next stage (unsteady flow module), in order to define the boundary conditions, an analysis of the hydrological data of the Danube was performed, which required the description of the water flow history for a period of time. Figures 6 and 7 show two cross-sections used for model calibration, which were made on both river parts.

Figure 6. Profile S1 created with the ADCP-profile trajectory, measured parameters (total flow, boat speed, average speed, measured distance, depth) and water velocity graph (velocities distributed on cells, with a colour gradient of velocity intensity, on the water column). The profile was extracted Appl. Sci.from2020 the, 10 River, 8327 Surveyor Live software used in the processing of data collected by ADCP. 11 of 28

Figure 7. ProfileProfile S3 created with the ADCP-profileADCP-profile trajectory, measured parameters (total flow, flow, boat speed, average speed, measured distance, depth) and water velocityvelocity graph (velocities distributed on cells, withwith aa colour colour gradient gradient of of velocity velocity intensity, intensity, on theon the water water column). column). The The profile profile was extractedwas extracted from the River Surveyor Live software (Version 4.1, SONTEK Company, San Diego, CA, USA, 2018) used in the processing of data collected by ADCP.

Following the GIS pre-processing of topo-bathymetric data, a new digital model of the minor riverbed was obtained. The cumulation of the two models resulted in a complex digital model with updated data on the minor riverbed. The next step was the analysis of flood scenarios using the dynamic method. For the dynamic method, HEC-RAS 5.0.3 specialized software was used. In the next step, the complex digital terrain model was introduced into the software for defining and processing the geometric data. The geometric data used for hydrodynamic simulation were obtained using high-precision DTM, with a spatial resolution of 0.5 m 0.5 m. The next geometric elements were the × channel centerline, bank lines, the flow-path lines above the banks, and 174 cross-section cut lines, perpendicular to the direction of the river flow. It is important to note that each cross-section covered the elevations of the stream bed to the banks, with a width of about 800 m to 1500 m. These geometric features were further used in the HEC-RAS hydraulic modeling step. An important step in hydraulic modeling is the assignment of the roughness values. The values of the Manning roughness coefficients were assigned according to the character of the riverbed, the relief, and the vegetation in the vicinity of the river. For the 2D flow area, the Manning roughness coefficient was calibrated in the range between 0.025 and 0.03. Regarding the calculation points of the 2D computational mesh, they were generated at steady intervals. The distance between the calculation points was set as a grid with a distance of 50 m on the x and y direction axes. In this way, finite elements were created for the 2D flow area. The floodplain attributes were provided by the topography of the high-resolution DTM. Thus, through the HEC-RAS 1D/2D hydraulic model, the 2D flow area was generated in the study area and upstream, by designing polygons along the length of the river, including the analyzed settlements and by defining the boundaries. By defining these boundaries, the program was able to calculate the spread of the flood wave. The design of the polygons was made taking into account the scenario in which the dyke of the settlements is breached with an opening of 20 m maintained for one day. Figures8 and9 show the breaches induced in the dyke of the targeted settlements. Reliable historical information is not available about the positions where dam breaching occurs. From this perspective, the locations assumed for dam breaching were established considering the most vulnerable dam zones. Appl. Sci. 2020, 10, 8327 12 of 28

These vulnerable areas were identiﬁed on the spot in the ﬁeld. Thus, the locations of the breaches were Appl. Sci. 2020, 10, x FOR PEER REVIEW 12 of 28 establishedAppl. Sci. 2020, and 10, x areFOR presented PEER REVIEW inTable 2. 12 of 28

Figure 8. Rupture scenarios (breaches) in the dykes of the settlements Partizani, Ilganii de Sus, FigureFigure 8. 8.Rupture Rupture scenarios scenarios (breaches) (breaches) in the in dykesthe dykes of the of settlements the settlements Partizani, Partizani, Ilganii de Ilganii Sus, Vulturu, de Sus, Vulturu, and Maliuc: the upper line of subfigures show the location of the breaches on ortophotomap, andVulturu Maliuc:, and the Maliuc upper: the line upper of subﬁgures line of subfigures show the show location the location of the breaches of the breaches on ortophotomap, on ortophotomap, second second line of subfigures shows the breaches in the dyke on DTM representation. linesecond of subﬁgures line of subfigures shows the shows breaches the breaches in the dyke in the on dyke DTM on representation. DTM representation.

Figure 9. Rupture scenarios (breaches) in the dykes of the settlements Mila 23, Gorgova, Crișan, and Figure 9. RuptureRupture scenarios scenarios (breaches) (breaches) in inthe the dykes dykes of ofthe the settlements settlements Mila Mila 23, Gorgova, 23, Gorgova, Crișan Cri, sand, an, andSulina Sulina:: the upper the upper line of line subfigures of subﬁgures show show the location the location of the of breaches the breaches on ortophotomap, on ortophotomap, second second line Sulina: the upper line of subfigures show the location of the breaches on ortophotomap, second line lineof subfigures of subﬁgures shows shows the breaches the breaches in the in dyke the dyke on DTM on DTM representation. representation. of subfigures shows the breaches in the dyke on DTM representation. Table 2. Table 2. The locations of the breaches considered in the dyke.dyke. Table 2. The locations of the breaches considered in the dyke. Settlement Partizani IlganiiIlganii de Sus Vulturu Maliuc Mila 23 Gorgova Cris, an Sulina Settlement Partizani Ilganii Vulturu Maliuc Mila 23 Gorgova Crișan Sulina SettlementLatitude 45Partizani◦11037” de45◦ 11Sus041” Vulturu45◦10027” Maliuc 45◦10029” Mila 45◦13 23043” Gorgova 45◦10040” 45Cri◦10șan029” 45Sulina◦09032” Longitude 28 57 20” 28de 57Sus15” 29 03 48” 29 07 04” 29 14 40” 29 09 27” 29 22 41” 29 38 01” Latitude 45°11′37◦ 0 ″ 45°11′41’’◦ 0 45°10′27◦ 0″ 45°10′29◦ 0 ″ 45°13′43◦ 0 ″ 45°10′40◦ 0 ″ 45°10′29◦ 0 ″ 45°09′32◦ 0 ″ Latitude 45°11′37″ 45°11′41’’ 45°10′27″ 45°10′29″ 45°13′43″ 45°10′40″ 45°10′29″ 45°09′32″ Longitude 28°57′20″ 28°57′15″ 29°03′48″ 29°07′04″ 29°14′40″ 29°09′27″ 29°22′41″ 29°38′01″ Longitude 28°57′20″ 28°57′15″ 29°03′48″ 29°07′04″ 29°14′40″ 29°09′27″ 29°22′41″ 29°38′01″ Based on the river flooding of 6 July 2010, a flow hydrograph of maximum discharge 16,600 m3/s was validated.Based on theThe river hydrological flooding of data 6 July were 2010, completed a flow hydrograph with information of maximum collected discharge from the 16,600 existing m3/s Based on the river flooding of 6 July 2010, a flow hydrograph of maximum discharge 16,600 m3/s hydrologicalwas validated. stations The hydrological in the Danube data Delta.were completedTable3 presents with information the maximum collected water levelsfrom the from existing 2010 was validated. The hydrological data were completed with information collected from the existing (withhydrological reference stations to the Black in the Sea Danube Sulina and Delta. the Black Table Sea 3 1975)presents of the the Danube maximum near thewater examined levels settlements.from 2010 hydrological stations in the Danube Delta. Table 3 presents the maximum water levels from 2010 (with reference to the Black Sea Sulina and the Black Sea 1975) of the Danube near the examined (with reference to the Black Sea Sulina and the Black Sea 1975) of the Danube near the examined settlements.Table 3. Maximum water levels for the year 2010 in meters Black Sea 75 (BS75) and the Black Sea settlements.Sulina (BSS) of the Danube near the settlements. Table 3. Maximum water levels for the year 2010 in meters Black Sea 75 (BS75) and the Black Sea SettlementsTable 3. MaximumMaximum water levels forMaximum the year 2010 in Settlementmeters Black Sea Maximum75 (BS75) and theMaximum Black Sea Sulina (BSS) of the Danube near the settlements. Sulina(Sulina (BSS) of theWater Danube Level near theWater settlements. Level (Sulina Water Level Water Level Branch) (Meters BS75) (Meters BSS) Branch) (Meters BS75) (Meters BSS) Settlements Maximum Maximum Settlement Maximum Maximum Settlements Maximum Maximum Settlement Maximum Maximum (SulinaPartizani Water 4.32 Level Water 4.54 Level (Sulina Gorgova Water 3.11 Level Water 3.33 Level Ilganii(Sulina de SusWater 4.32 Level Water 4.54 Level (Sulina Mila 23 Water 2.70 Level Water 2.92 Level Branch) (Meters BS75) (Meters BSS) Branch) (Meters BS75) (Meters BSS) BranchVulturu) (Meters 3.61 BS75) (Meters 3.83 BSS) Branch Cris, an) (Meters 2.10 BS75) (Meters 2.32 BSS) Partizani 4.32 4.54 Gorgova 3.11 3.33 PartizaniMaliuc 4.32 3.43 4.54 3.65 Gorgova Sulina 1.243.11 1.463.33 Ilganii de Ilganii de 4.32 4.54 Mila 23 2.70 2.92 Sus 4.32 4.54 Mila 23 2.70 2.92 AfterSus the boundary conditions were accomplished (upstream: the flow hydrograph and downstream: Vulturu 3.61 3.83 Crișan 2.10 2.32 the normalVulturu depth), the process3.61 was validated.3.83The input valueCrișan for the normal2.10 depth was 0.04.2.32 This value Maliuc 3.43 3.65 Sulina 1.24 1.46 wasMaliuc the energy slope3.43 that was used in3.65 the softwareSulina for calculating the1.24 normal depth.1.46 For this

After the boundary conditions were accomplished (upstream: the flow hydrograph and After the boundary conditions were accomplished (upstream: the flow hydrograph and downstream: the normal depth), the process was validated. The input value for the normal depth downstream: the normal depth), the process was validated. The input value for the normal depth was 0.04. This value was the energy slope that was used in the software for calculating the normal was 0.04. This value was the energy slope that was used in the software for calculating the normal depth. For this study, the energy slope was approximated by using the average slope of the water depth. For this study, the energy slope was approximated by using the average slope of the water

Appl. Sci. 2020, 10, 8327 13 of 28 study, the energy slope was approximated by using the average slope of the water surface near the cross-sections. The next stage defined the start and end period of the simulation. Several flow hydrographs were applied in the software, corresponding to the flood stages, to obtain several flood scenarios (32 scenarios in total). The flood stages are presented in Table4.

Table 4. Sets of ﬂooding water levels in meters with reference to the Black Sea 75 (BS75) and the Black Sea Sulina (BSS), applied to the settlements.

Water Level–Sulina Branch 32 Scenarios Reference Reference Localities Step 1 Step 2 Step 3 Step 4 Localities Step 1 Step 2 Step 3 Step 4 System System (m) BS75 2.50 3.10 3.70 4.32 (m) BS75 2.50 2.70 2.90 3.11 Partizani Gorgova (m) BSS 2.72 3.32 3.92 4.54 (m) BSS 2.72 2.92 3.12 3.33 Ilganii de (m) BS75 2.95 3.35 4.15 4.32 (m) BS75 1.75 2.15 2.55 2.70 Mila 23 Sus (m) BSS 3.17 3.57 4.37 4.54 (m) BSS 1.97 2.37 2.77 2.92 (m) BS75 2.25 2.85 3.45 3.61 (m) BS75 1.60 1.80 2.00 2.10 Vulturu Cris, an (m) BSS 2.47 3.07 3.67 3.83 (m) BSS 1.82 2.02 2.22 2.32 (m) BS75 2.25 2.65 3.05 3.43 (m) BS75 0.75 0.95 1.15 1.24 Maliuc Sulina (m) BSS 2.47 2.87 3.27 3.65 (m) BSS 0.97 1.17 1.37 1.46

For each settlement, four ﬂooding stages were applied with intervals between 60 cm and 62 cm (Partizani settlement), 17, 40, and 80 cm (Ilganii de sus), 16 and 60 cm (Vulturu), 38 and 40 cm

(Maliuc), 20 and 21 cm (Gorgova), 15 and 40 cm (Mila 23), 20 cm (Cris, an), and 9 and 20 cm (Sulina). Flooding stages were presented in the Sulina Black Sea reference system and the Black Sea 75 system. Flooding stages were calculated from the lowest flooding level to the highest flooding level for each analyzed settlement. Thus, different flood scenarios of the settlements for different water levels in the river next to them could be visualized. In the software, the hydraulic calculations were performed on 06 July 2010 at 10:00, for 24 h (the timeframe in which the flooding breach is maintained in the dyke for the dynamic method). Thus, 32 scenarios were run by execution of the unsteady flow simulator, the pre-processor geometry, and the output post-processor, finally obtaining the results by the first method (dynamic). The process of hydraulic modeling involves the introduction and use of a large number of data, both geographical and hydraulic [36]. After completing the data entry process and validating the model (including resolving the data entry errors), the program was run and the results of the hydraulic modeling were obtained. The results of hydraulic modeling can be visualized in several ways: maps, graphs, or tables. In the present study the results were displayed in the form of the flooding maps. For the static method Global Mapper v17.0.2 specialized software was used. Less data were processed with this method than with the previous one. Geographic data, regional boundaries, and orthophotomaps were processed, but mostly the high-resolution digital terrain model. For the correct processing of the information, and to select the study area, the digital terrain model was introduced into the software, and translated into the suitable reference system (STEREO 70, EPSG: 31,700 with the Black Sea 75 system for elevation points). The next step was the individual clipping of the area of interest from the digital terrain model (a sufficiently extensive perimeter was chosen to perform the simulation; this perimeter covered the administrative territorial unit and the adjacent territory for each settlement). The flow area for each settlement was defined by establishing the regional limits (Figure 10). In the next step, using the water rise calculation function, the resolution of the analyzed grid was set to 5 m in both main directions, x and y. This was where the water level increase amounts, using the flooding levels established in Table4, were also introduced. Thereby, the information corresponding to the maximum levels reached in 2010 (in the Black Sea 75 reference system) was superimposed over the digital terrain model. The application of this method corresponded to the most unfavourable Appl. Sci. 2020, 10, 8327 14 of 28 scenario, by maintaining breaches in the flood dyke of settlements for a very long time (more than 24Appl. h). Sci. Thus, 2020, 3210, scenariosx FOR PEER were REVIEW run for the analyzed localities. 14 of 28

FigureFigure 10.10. TheThe boundaryboundary representationrepresentation ofof thethe ﬂowflow surfacesurface forfor eacheach settlements:settlements: Partizani,Partizani, IlganiiIlganii dede

Sus,Sus, Vulturu,Vulturu, Maliuc,Maliuc, Mila Mila 23, 23, Gorgova, Gorgova, Cris Cri, șananandand Sulina. Sulina.

TheIn the results next werestep, generatedusing the inwater the formrise c ofalculation flooding function, maps with the flooded resolution areas of for the each analyzed settlement, grid andwas wereset to largely 5 m in based both onmain the directions digital terrain, x and model. y. This was where the water level increase amounts, usingGIS the software flooding was levels used established to adapt these in Table materials 4, were to the also requirements introduced. of Thereby, the hydraulic the information modeling softwarecorresponding and to to the the size maximum of the study levels area. reached The study in 2010 area (in being the Black large, Sea the 75 size reference of the files system) used waswas considerable,superimposed which over madethe digital it diffi terraincult to processmodel. The the dataapplication at a later of stage, this method and to introduce correspond geographiced to the datamost into unfavourable the specialized scenario software., by maintaining For the development breaches in ofthe the flood hydraulic dyke of model settlements of the Danubefor a very Delta, long twotime types (more of than materials 24 h). wereThus, collected 32 scenarios as cartographic were run for support. the analyzed The first localities. type of material included the digitalThe terrain results model, were and generated the ortho in the photomaps form of flooding of the Danube maps Delta.with flooded The second areas type for each of material settlement was, lessand recent,were largely being topographicbased on the maps digital made terrain in 1965. model. These two materials were refined and processed in the firstGIS phase software in GIS was (ArcMAP used to 10.4 adapt specialized these materials software), to the to bring requirements them into of the the appropriate hydraulic projectionmodeling system.software The and projection to the size used of the was study STEREO area. 70,The with study the area reference being large, system the Black size Seaof the 75 forfiles elevations. used was Basedconsiderable, on these which materials, made ait contourdifficult wasto process drawn the for data the delimitationat a later stage of, theand studyto introduce area, both geographic for the hydraulicdata into the modeling specialized and for software. the GIS For analysis. the development In the case of of the the hydraulic hydraulic modeling, model of thethe areaDanube taken Delta, into accounttwo types spread of materials over the were Tulcea collected and Sulina as cartographic branches, and support. in the The case first of the type GIS ofanalysis, material theinclude aread was the limiteddigital terrain to the administrative–territorialmodel, and the ortho photomaps unit of each of the settlement Danube Delta. on the The Sulina second branch. type of material was less recent, being topographic maps made in 1965. These two materials were refined and processed 5.in Discussion the first phase in GIS (ArcMAP 10.4 specialized software), to bring them into the appropriate projectionAfter runningsystem. theThe software,projection 64 used flood was scenarios STEREO resulted 70, with (32 the scenarios reference for system each method), Black Sea and 75 thefor mostelevations. relevant Based results on these were materials, extracted a forcontour the eight was drawn settlements for the from delimitation the Danube of the Delta study targeted area, both in thefor presentthe hydraulic work. modeling The results and were for mapsthe GIS illustrating analysis. theIn the potentially case of the floodable hydraulic areas modeling, of the analyzed the area settlementstaken into account in the Danube spread Delta. over the In more Tulcea detail, and theSulina flooding branches, maps and obtained in the in case the of present the GIS study analysis, were overviewthe area was maps limited that showed, to the administrative for each flooding–territorial level considered, unit of each the settlement flood boundary, on the whichSulina representsbranch. the extent of the water for each case (scenario) considered, and the depth or water level, depending on the5. Discussion method. TheAfter flooding running maps the software resulting, from64 flood the applicationscenarios resulted of the dynamic (32 scenarios method for had each a depthmethod), class and of 0 the to 2most m. The relevant flood results maps resulting were extracted from the for static the eight method settlements established from the the depth Danube as the Delta difference targeted between in the thepresent considered work. water The results level of were the floodedmaps illustr surfaceating and the the potentially ground level floodable (the elevation areas of of the the terrain analyz oned thesettlements digital terrain in the modelDanube expressed Delta. In inmore the detail, Black Seathe flooding reference maps system obtained 75). in the present study were overviewIn approaching maps that the show dynamiced, method, for each the flooding hydraulic level scenario considered, set was developed the flood using boundary, the hydraulic which modelrepresents of the the Danube extent of Delta the water to determine for each the case flood (scenario) surfaces considered for each,settlement. and the depth In bothor water methods, level, thedepending historical on maximum the method. level reached in the summer of 2010 was considered for all localities. The results of applyingThe flooding the maximum maps resulting level were from reflected the application in the flood–stageof the dynamic IV. Subsequently,method had a todepth obtain class flood of 0 scenariosto 2 m. The at lower flood water maps levels resulting than in from 2010, the flood stat stagesic method (Table established4) were applied the depth (the di asfferences the difference being a fewbetween centimetres the considered or tens of water centimetres level of from the flooded the peak), surface and so and several the ground flooding level map (the scenarios elevation resulted of the forterrain each on settlement the digital analyzed, terrain model as presented expressed in in Figures the Black 11 –Sea13. reference All of these system in case 75). of failure of the In approaching the dynamic method, the hydraulic scenario set was developed using the hydraulic model of the Danube Delta to determine the flood surfaces for each settlement. In both methods, the historical maximum level reached in the summer of 2010 was considered for all localities. The results of applying the maximum level were reflected in the flood–stage IV. Subsequently, to obtain flood scenarios at lower water levels than in 2010, flood stages (Table 4) were

Appl. Sci. 2020, 10, x FOR PEER REVIEW 15 of 28

applied (the differences being a few centimetres or tens of centimetres from the peak), and so several flooding map scenarios resulted for each settlement analyzed, as presented in Figures 11–13. All of these in case of failure of the protective dam. The scenarios in flood stage I corresponded to the lowest level of flooding, while the scenarios in flood stage IV corresponded to the maximum level of flooding, and the scenarios in flood stages II and III corresponded to the intermediate levels of flooding, between the maximum and the lowest level. The most useful way to visualize the results of the hydraulic modeling and GIS processing was with the help of maps, thus being able to refer both to the development in time and in space of the phenomenon. With the help of the hydraulic modeling software, in the first phase, the flood limits (as maps) and the flooding process could be extracted (the scenarios can also be viewed as video). Next, with these results, further processing or interpretation was done in GIS, and other specialized programs. The results were presented by Ras Mapper, using the hydraulic model 1D/2D HEC-RAS at high resolution (DTM 0.5 m × 0.5 m). Figures 11–13 present the propagation trend of the flood surface

Appl.depths Sci. over2020, 10the, 8327 flooded area, after 24 h from the beginning of the simulation. The elevations used15 of 28in the data processing with the dynamic method were referenced to the Black Sea 75 system (BS75). The results obtained from the application of the dynamic method identified flooding areas and water protectivedepths close dam. to the The settlements scenarios in in flood the situation stage I corresponded of maintaining to thethe lowestbreach levelin the of dyke flooding, for a whileperiod the of scenarios24 h. in flood stage IV corresponded to the maximum level of flooding, and the scenarios in floodThis stages method II and is III much corresponded more complex to the thanintermediate the static levels method, of flooding, as it requires between a wider the maximum range of andspecific the lowestdata and level. knowledge The most from useful the way operator. to visualize If the the main results concern of the is hydraulic to anticipate modeling the cur andrent GIS or processingfuture behaviour, was with obtaining the help relevant of maps, field thus data being isable an important to refer both and to integral the development part of the in modeling time and inprocess space of[36] the. Good phenomenon. results depend With the on help the of exact the hydraulic formulation modeling of the software, problem, in and the firstthe correct phase, theidentification flood limits of (as the maps) main and parameters the flooding that processinfluence could the investigated be extracted (thephenomena. scenarios Analysing can also be the viewed most asunfavorable video). Next, scenario with these(flood results, stage IV further-maximum processing water orlevel), interpretation there were was several done floodable in GIS, and surfaces other specializedinside the settlements programs. (Table 5).

FigureFigure 11. 11. Flood scenarios maps for Partizani (first(first column group of subfigures), subfigures), Ilganii Ilganii de de Sus Sus (second(second columncolumn groupgroup ofof subfigures),subfigures), and Vulturu (third(third columncolumn groupgroup ofof subfigures)subfigures) settlements,settlements, correspondingcorresponding toto thethe conditioncondition ofof 77 JulyJuly 2010,2010, byby using the 1D//2D2D HEC-RASHEC-RAS model,model, atat high resolution Appl.DTM Sci. 20 0.520 m, 10, x0.5 FOR m, PEER dynamic REVIEW method. 16 of 28 DTM 0.5 m ×× 0.5 m, dynamic method.

FigureFigure 12. 12.Flood Flood scenarios scenarios maps maps for forMaliuc Maliuc (first (first column column group group of subfigures), of subfigures) Mila, Mila23 (second 23 (second column groupcolumn of subfigures), group of subfigures)and Gorgova, and (third Gorgova column (third group column of subfigures) group settlements, of subfigures) corresponding settlements, to the condition of 7 July 2010, by using the 1D/2D HEC-RAS model, at high resolution DTM 0.5 m 0.5 m, corresponding to the condition of 7 July 2010, by using the 1D/2D HEC-RAS model, at high resolution× dynamicDTM 0.5 method. m × 0.5 m, dynamic method.

Figure 13. Flood scenarios maps for Crișan (first column group of subfigures) and Sulina (second column group of subfigures) settlements, corresponding to the condition of 7 July 2010, by using the 1D/2D HEC-RAS model, at high resolution DTM 0.5 m × 0.5 m, dynamic method.

For the dynamic method the modeled flooded area was calculated using ArcGIS software. From RAS Mapper–Animator, it was found that the maximum flooded areas at flood stage IV was 5.79 km2 (for all settlements), the simulation taking place between 10:00 of 6 July 2010 and 10:00 of 7 July 2010.

Appl. Sci. 2020, 10, x FOR PEER REVIEW 16 of 28

Figure 12. Flood scenarios maps for Maliuc (first column group of subfigures), Mila 23 (second column group of subfigures), and Gorgova (third column group of subfigures) settlements, Appl. Sci.corresponding2020, 10, 8327 to the condition of 7 July 2010, by using the 1D/2D HEC-RAS model, at high resolution16 of 28 DTM 0.5 m × 0.5 m, dynamic method.

Figure 13.13. FloodFlood scenarios scenarios maps maps for for Cri Cris, anșan (first (first column column group group of subfigures) of subfigures) and Sulina and (secondSulina (second column groupcolumn of group subfigures) of subfigures) settlements, settlements, corresponding corresponding to the condition to the condition of 7 July of 2010, 7 July by 2010, using by the using 1D /the2D HEC-RAS model, at high resolution DTM 0.5 m 0.5 m, dynamic method. 1D/2D HEC-RAS model, at high resolution DTM× 0.5 m × 0.5 m, dynamic method.

TheFor the results dynamic were method presented the by modeled Ras Mapper, flooded using area the was hydraulic calculated model using 1D Ar/2DcGIS HEC-RAS software. at From high resolution (DTM 0.5 m 0.5 m). Figures 11–13 present the propagation trend of the flood surface RAS Mapper–Animator,× it was found that the maximum flooded areas at flood stage IV was 5.79 km2 depths(for all settlement over the floodeds), the simulation area, after taking 24 h from place the between beginning 10:00 of of the 6 July simulation. 2010 and The 10:00 elevations of 7 July 2010. used in the data processing with the dynamic method were referenced to the Black Sea 75 system (BS75). The results obtained from the application of the dynamic method identified flooding areas and water depths close to the settlements in the situation of maintaining the breach in the dyke for a period of 24 h. This method is much more complex than the static method, as it requires a wider range of specific data and knowledge from the operator. If the main concern is to anticipate the current or future behaviour, obtaining relevant field data is an important and integral part of the modeling process [36]. Good results depend on the exact formulation of the problem, and the correct identification of the main parameters that influence the investigated phenomena. Analysing the most unfavorable scenario (flood stage IV-maximum water level), there were several floodable surfaces inside the settlements (Table5). For the dynamic method the modeled flooded area was calculated using ArcGIS software. From RAS Mapper–Animator, it was found that the maximum flooded areas at flood stage IV was 5.79 km2 (for all settlements), the simulation taking place between 10:00 of 6 July 2010 and 10:00 of 7 July 2010. The flood surface depth reached up to 1.94 m (flood stage IV Partizani settlement), 1.85 m (flood stage IV Ilganii de Sus), 1.91 m (flood stage IV Vulturu settlement), 1.89 m (flood stage IV Gorgova settlement), 1.66 m (flood stage IV Mila 23 settlement), 1.90 m (Gorgova settlement), 1.95 m

(Cris, ansettlement), and 1.47 m (Sulina settlement). Appl. Sci. 2020, 10, 8327 17 of 28

Table 5. The ﬂooded areas of the settlements analyzed at the ﬂood stage IV: maximum water level of the Danube.

Maximum Dynamic Method Static Method Settlement Water Level Floodable Flooded Floodable Flooded Difference Flood Stage Appl. Sci. 2020, 10, x FOR PEER REVIEW Surfaces Areas Surfaces Areas % 17 of 28 IV (km2) (%) (km2) (%) The floodPartizani surface depth 4.32 reached m BS75 up to 1.681.94 m (flood 93.92% stage IV Partizani 1.76 settlement), 98.39% 1.85 m 4.47%(flood stage IV IlganiiIlganii de de Sus Sus), 1.91 4.32 m m BS75 (flood stage 0.49 IV Vulturu 87.81% settlement), 0.54 1.89 m 96.77% (flood stage IV 8.96% Gorgova Vulturu 3.61 m BS75 0.33 79.44% 0.40 96.29% 16.85% settlement),Maliuc 1.66 m 3.43 (flood m BS75 stage IV 0.18 Mila 23 settlement), 82.57% 1.90 0.20 m (Gorgova 91.74% settlement), 9.17% 1.95 m (CrișanGorgovasettlement), and 3.11 1.47 m BS75 m (Sulina 0.71 settlement). 87.22% 0.75 92.14% 4.92% FiguresMila 23 12–14 indicate 2.70 m BS75that the areas 0.77 flooded 82.53%under the four 0.86 flood stages 92.18% in the dynamic 9.65% method, varied betweenCris, an 0.8 km 2.102– m1.68 BS75 km2 (Partizani), 0.34 0.26 71.03% km2–0.49 km 0.402 (Ilganii de 83.56% Sus), 0.80 km 12.53%2–0.33 km2 Sulina 1.24 m BS75 1.29 63.58% 1.60 78.86% 15.28% (Vulturu), 0.025 km2–0.18 km2 (Maliuc), 0.8 km2–0.71 km2 (Gorgova), 0.21 km2–0.77 km2 (Mila 23), 0.05 km2–0.34 km2 (Crișan), and 0.15 km2–1.29 km2 (Sulina). From the results of the simulations performed we noticeFiguresd that 12– 14the indicate flood extension that the areascover floodeded households under the, infrastructure four flood stages elements in the, and dynamic vacant method, land in 2 2 2 2 2 2 varieddifferent between proportions 0.8 km from–1.68 one km flood(Partizani), stage to another. 0.26 km It–0.49 can be km seen(Ilganii in Figures de Sus), 11– 0.8013 that km water–0.33 flows km 2 2 2 2 2 2 (Vulturu),in localities 0.025 from km one–0.18 zone kmto another,(Maliuc), following 0.8 km the–0.71 slope km of(Gorgova), the natural 0.21topography. km –0.77 The km widespread(Mila 23), 2 2 2 2 0.05use and km –0.34role of km hydraulic(Cris, an), models and 0.15 have km changed–1.29 km in the(Sulina). recent years, From themainly results due of to the the simulations significant performedadvances in we computer noticed that modeling. the flood They extension remain covered an important households, modeling infrastructure tool, especially elements, in andthe vacantdesign landof hydraulic, in different river proportions structures from, and one coastal flood engineering stage to another. application It cans, beas seenwell inas Figures for environment 11–13 thatal waterprotection flows, or in in localities providing from physical one zone input to data another, for mathematical following the modeling slope of [46] the. natural topography. The widespreadFigure 14 shows use and the roleresults of hydraulic of the stat modelsic method. have The changed results inare the presented recent years, in the mainly form of due flooding to the significantmaps that advancesrepresent inthe computer potentially modeling. floodable They areas remain of the an analy importantzed settlements modeling tool,in the especially Danube inDelta the designat flood of stage hydraulic, IV (the river maximum structures, water and level), coastal in engineering the situation applications, of maintaining as well the as breach for environmental in the dyke protection,for more than or in24 providing h. physical input data for mathematical modeling [46].

FigureFigure 14. TheThe representation representation ofof digital digital maps maps with with flooded ﬂooded areas areas for settlements: for settlements: (a)—Partizani, (a)—Partizani, (b)—

(Crib)—Crișan, (s,can,)— (Maliuc,c)—Maliuc, (d)— (Ilganiid)—Ilganii de sus, de sus, (e)— (eGorgova,)—Gorgova, (f)— (fVulturu,)—Vulturu, (g)— (gSulina,)—Sulina, (h)— (hMila)—Mila 23, 23, by byusing using GIS GIS analysis, analysis, at high at high resolution resolution DTM DTM 0.5 0.5 m m× 0.5 0.5m, m,static static method. method. ×

FigureThe results 14 shows obtained the results following of the the static application method. of The the results static aremethod presented highlight in the the form flood of floodingsurfaces mapsin the thatsettlements represent corresponding the potentially to floodable the maximum areas ofwater the analyzedlevel of the settlements Danube, this in the situation Danube being Delta the at floodmost stageunfavorable IV (the (the maximum water level water on level), the Danube in the situation being the of s maintainingame as the water the breach level in the localities dyke for moretargeted, than by 24 h.keeping the breach in the protective dam for a longer period). This method is less complexThe resultsthan the obtained dynamic following method, the as applicationit requires a of smaller the static set method of specific highlight data and the knowledge flood surfaces from in thethe settlementsoperator. By corresponding the analysis of to the the most maximum unfavorable water level scenario, of the floodable Danube, thissurfaces situation inside being the localities the most unfavorableresulted, and (the they water are presented level on the in DanubeTable 5. being the same as the water level in the localities targeted, by keeping the breach in the protective dam for a longer period). This method is less complex than the Table 5. The flooded areas of the settlements analyzed at the flood stage IV: maximum water level of the Danube.

Dynamic Method Static Method Maximum Floodable Flooded Floodable Flooded Difference Settlement Water Level Surfaces Areas Surfaces Areas % Flood Stage IV (km2) (%) (km2) (%) Partizani 4.32 m BS75 1.68 93.92% 1.76 98.39% 4.47% Ilganii de Sus 4.32 m BS75 0.49 87.81% 0.54 96.77% 8.96%

Appl. Sci. 2020, 10, x FOR PEER REVIEW 18 of 28

Vulturu 3.61 m BS75 0.33 79.44% 0.40 96.29% 16.85% Maliuc 3.43 m BS75 0.18 82.57% 0.20 91.74% 9.17% Appl. Sci.Gorgova2020, 10 , 8327 3.11 m BS75 0.71 87.22% 0.75 92.14% 4.92%18 of 28 Mila 23 2.70 m BS75 0.77 82.53% 0.86 92.18% 9.65% Crișan 2.10 m BS75 0.34 71.03% 0.40 83.56% 12.53% dynamicSulina method, as1.24 it requires m BS75 a smaller1.29 set of specific63.58% data and knowledge1.60 from78.86% the operator.15.28% By the analysis of the most unfavorable scenario, floodable surfaces inside the localities resulted, and they are presentedThe differences in Table5. between the two methods are summarized in Table 5. For the static method the floodedThe area differences was calculated between using the two Global methods Mapper are software. summarized From in the Table software,5. For the it was static found method that the the floodedmaximum area flooded was calculated areas at flood using stage Global IV was Mapper 6.51 software.km2. The flood From surface the software, depth reached it was found up to that4.08 them (flood maximum stage floodedIV Partizani areas settlement), at flood stage 3.46 IV m was (flood 6.51 stage km2 .IV The Ilganii flood de surface Sus), 2.85 depth m (flood reached stage up IV to 4.08Vulturu m (flood settlement), stage IV 2.64Partizani m (flood settlement), stage IV 3.46 Gorgova m (flood settlement), stage IV Ilganii 2.61 de m Sus), (flood 2.85 stage m (flood IV Mila stage 23 IVsettlement), Vulturu settlement), 2.59 m (Gorgova 2.64 m settlement), (flood stage 2.06 IV m Gorgova (Crișansettlement), settlement), and 2.61 1.65 m (flood m (Su stagelina settlement). IV Mila 23 settlement),For some 2.59 settlements m (Gorgova the settlement), differences 2.06 were m (Cris significant,, ansettlement), considering and 1.65 that m (Sulina the second settlement). method (static)For involve some settlementsd maintaining the ditheff erencesbreach for were a long significant, enough considering time to flood that at themaximum second methodlevels all (static) areas involvedbelow this maintaining level within the the breach settlements. for a long In enough the case time of to the flood first at maximummethod’s (dynamic) levels all areas analysis, below with this level1D/2D within hydraulic the settlements. modeling, Inthe the actual case flooded of the first surfaces method’s were (dynamic) determined analysis, for a withshorter 1D /period2D hydraulic (24 h), modeling,resulting in the smaller actual flooded flooded surfaces. surfaces were determined for a shorter period (24 h), resulting in smaller floodedFollowing surfaces. the comparative results (applying flooding stage IV-maximum water level), it can be observedFollowing that 75% the of comparative localities (6 results out of (applying8) had more flooding than 91% stage of their IV-maximum surface flooded, water level), if the breaches it can be observedwere maintained that 75% for of localitiesmore than (6 a out day of in 8) the had dykes more of than the 91% settlements of their, surface and at flooded,maximum if thewater breaches levels wereof the maintained Danube. In for the more case thanof maintaining a day in the the dykes breaches of the for settlements, a period of and only at 1 maximumday, it was water observed levels that of theonly Danube. one out Inof theeight case settlements of maintaining considered the breaches had a flooded for a period area of of more only than 1 day, 90%. it was observed that only oneIn the out application of eight settlements of the two considered methods, hadfor some a flooded of the area settlements, of more than slightly 90%. bigger differences wereIn noticed the application regarding of the the two flooded methods, surface. for some The of most the settlements, significant slightly differences bigger appear differencesed for were the noticedsettlements regarding Vulturu, the flooded Crișan, surface.and Sulina, The most with significant differences di ff exceedingerences appeared 12%, and for reaching the settlements up to

Vulturu,approximately Cris, an, 17% and Sulina,in some with cases. diff Inerences order exceedingto analyze 12%,, in percentage, and reaching the up differences to approximately regarding 17% the in someflooded cases. areas In in order all the to analyze, other flood in percentage, stages (flood the stage differences I, flood regarding stage II, theand flooded flood stage areas III), in all some the othergraphs flood were stages designed, (flood stageshowing I, flood the stagedynamics II, and of flood the flooded stage III), areas some in graphs the scenarios were designed, analyzed showing for the thesettlements dynamics (Figures of the flooded 15 and 16). areas in the scenarios analyzed for the settlements (Figures 15 and 16).

FigureFigure 15. 15. The didifferencesﬀerences of ﬂooded flooded area area for for thethe analyzed analyzed scenarios scenarios byby using using static static method method and and dynamicdynamic methodmethod at:at: ((aa)—Partizani,)—Partizani, ((bb)—Ilganii)—Ilganii dede Sus,Sus, ((cc)—Vulturu,)—Vulturu, andand ((dd)—Maliuc)—Maliuc settlements. settlements.

TheThe dynamics of the the flooded flooded areas areas for for the the analy analyzedzed settlements settlements differ diffedered from from one one method method to the to theother. other. From From the displayed the displayed results, results, it is visible it is visible that by that applying by applying the static the method static method in all flood in all stages, flood stages,the flooded the flooded surfaces surfaces were larger were compared larger compared to the flooded to the floodedsurfaces surfacesobtained obtained by applying by applying the dynamic the dynamicmethod. method. FiguresFigures 1515 andand 1616 indicateindicate thatthat thethe areas floodedflooded underunder thethe fourfour floodflood stages in the static method, 2 2 2 2 2 2 variedvaried betweenbetween 0.850.85 kmkm2–1.76–1.76 kmkm2 (Partizani),(Partizani), 0.290.29 kmkm2–0.54–0.54 kmkm2 (Ilganii(Ilganii dede Sus),Sus), 0.110.11 km km2–0.40–0.40 kmkm2 (Vulturu), 0.04 km2–0.20 km2 (Maliuc), 0.11 km2–0.75 km2 (Gorgova), 0.23 km2–0.86 km2 (Mila 23), 2 2 2 2 0.08 km –0.40 km (Cris, an), and 0.28 km –1.60 km (Sulina). Appl. Sci. 2020, 10, x FOR PEER REVIEW 19 of 28

(Vulturu), 0.04 km2–0.20 km2 (Maliuc), 0.11 km2–0.75 km2 (Gorgova), 0.23 km2–0.86 km2 (Mila 23), Appl. Sci. 2020, 10, 8327 19 of 28 0.08 km2–0.40 km2 (Crișan), and 0.28 km2–1.60 km2 (Sulina).

FigureFigure 16. 16. The differencesdiﬀerences of ﬂoodedflooded area for the the analyzed analyzed scenarios scenarios by using using static static method method and and dynamicdynamic methodmethod at:at: (a)—Gorgova,)—Gorgova, (b)—Mila)—Mila 23,23, ((cc)—Crisan,)—Crisan, andand ((dd)—Sulina)—Sulina settlements.settlements.

ReferringReferring to thethe firstfirst floodflood stage,stage, thethe largestlargest percentagepercentage didifferencesfferences betweenbetween thethe twotwo methodsmethods regardingregarding thethe floodedflooded areaarea were observableobservable for Vulturu (di(differencefference ofof 9.63%)9.63%) andand MaliucMaliuc (di(differencefference ofof 9.17%).9.17%). The smallest percentagepercentage differences differences in the firstfirst floodflood stagestage werewere observableobservable forfor PartizaniPartizani (di(differencefference of of 1.68%) 1.68%) and and Gorgova Gorgova (di (differencefference of of 3.68%). 3.68%). For the second flood flood stage, stage, the largest largest percentages,percentages, in terms of the the di differencesfferences regarding regarding the the flooded flooded area, area, appearedappeared inin IlganiiIlganii de de Sus Sus (di(differencefference of of 12.55%), 12.55%), Vulturu Vulturu (di (differencefference of of 16.85%), 16.85%), Maliuc Maliuc (di (differencefference of of 13.76%), 13.76%), andand Cri Crisșan, an (di(differencefference ofof 10.44%),10.44%), andand thethe smallestsmallest didifferencefference waswas foundfound inin MilaMila 2323 (di(differencefference ofof 4.30%).4.30%). InIn thethe thirdthird floodflood stage,stage, thethe largestlargest percentagepercentage didifferencesfferences regardingregarding thethe floodedflooded areaarea werewere recordedrecorded inin thethe settlementssettlements of Cri Crișans, an (difference (difference of of 21.89%) 21.89%) and and Sulina Sulina (difference (difference of of21.20%). 21.20%). At Atthe thesame same time, time, the thesmallest smallest percentage percentage differences differences were were present present in in Partizani Partizani (difference (difference of of 9.46%), 9.46%), Ilganii Ilganii de SusSus (di(differencefference ofof 8.96%),8.96%), VulturuVulturu (di (differencefference of of 9.63%), 9.63%), and and Gorgova Gorgova (di (differencefference of of 9.83%). 9.83%). TheThe dynamicdynamic methodmethod givesgives thethe lowestlowest percentagepercentage ofof floodedflooded areasareas forfor allall thethe settlements.settlements. ThisThis isis duedue toto the fact that the dynamic method involves floodingflooding the surfacessurfaces gradually,gradually, andand dependingdepending onon thethe topography of of the the terrain. terrain. Gradual Gradual surface surface flooding flooding is isinduced induced by by the the flow flow of the of the river, river, and and so the so thearea area flooded flooded by considering by considering the dynamic the dynamic method method at water at water levels levels such as such those as thoseshown shown in flood in stages flood stagesII and III, II and was III, smal wasler smaller than the than area the flooded area flooded by considering by considering the static the method. static method. In addition In additionto this fact, to thisthe physicalfact, the– physical–geographicalgeographical factors had factors a certain had a influence certain influence on the process on the process of flood of leakage flood leakage in the indynamic the dynamic method. method. Here the Here relief the influences relief influences the size the, and size, especially and especially the distribution the distribution,, of the of flood the floodleakage leakage by its by degree its degree of fragmentation of fragmentation and and by bythe the size size of of the the slope slope of of the the land land in in the localities localities considered.considered. If we analy analyzeze the evolution of the floodflood at all floodflood stages, in Figures 11 and 12 wewe cancan 2 seesee thatthat thethe floodedflooded areaarea increasedincreased sharplysharply inin thethe localitieslocalities VulturuVulturu (from(from 0.100.10 kmkm2 inin floodflood stagestage IIII toto 2 2 2 0.290.29 km2 inin flood flood stage stage III), III), and and Maliuc Maliuc (from (from 0.04 0.04 km km2 in inflood flood stage stage III IIIto 0.18 to 0.18 km km2 in floodin flood stage stage IV). IV).In the In case the caseof the of settlements the settlements Partizani Partizani and Ilganii and Ilganii de Sus, de the Sus, flooded the flooded area show areaed showed a constant a constant increase increasein all flood in allstages. flood This stages. was Thisdue to was the due fact to that the the fact land that surface the land tends surface to a tendssmoother to a slope smoother compared slope comparedto Vulturu toand Vulturu Maliuc and settlements, Maliuc settlements, where the whereslope in the some slope areas in some becomes areas steep. becomes At the steep. same At time the samethere timeare other there factors are other that factors influence thatd influenced the process the of process flood leakage of flood in leakage the dynamic in the dynamic method,method, such as suchvegetation, as vegetation, which has which an important has an important role in the role leakage in the leakageprocess. process. On one Onhand one by hand influencing by influencing the soil theformation soil formation,, and on andthe onother the hand other contribut hand contributinging to increas to increasinging capacity capacity of infiltra of infiltrationtion in the in soil the and soil andreducing reducing soil soil erosion. erosion. Another Another important important factor factor is is the the land land use. use. This This essentially essentially and negatively negatively influencesinfluences floodflood leakageleakage conditions.conditions. Furthermore the anthropic factor has an importantimportant role onon thethe formationformation of of flood flood leakage, leakage, specifically specificall throughy through the changes the changes brought brought to the to natural the natural system: system: the change the ofchange the land of the cover land (natural cover vegetation(natural vegetation was replaced was byreplaced agricultural by agricultural crops (the mostcrops common(the most case common in the Danubecase in the Delta), Danube orchards, Delta), vineyards, orchards, vineyards and human, and settlements), human settlements), and changes and to changes the Danube to the riverbed Danube toriverbed ensure to the ensure necessary the necessary water (for water irrigation, (for irrigation, water needs,water needs, electricity, electricity, construction construction materials, materials, etc.), road networks, flood protection works, etc. As a result of the increasing intervention of man in the

Appl. Sci. 2020, 10, 8327 20 of 28 natural systems, their natural evolution is disturbed, the changes being significant or fundamental. In the latter case, the intervention causes breaks in the rhythm of the natural development, imbalances that accentuate the involution of the respective landscapes. The comparative analysis of the results highlighted, first of all, the substantial effect of reducing the flooded areas, at a lower flood level of the Danube, with differences from one settlement to another. The flooding maps for the analyzed settlements provide information on the extent of floodable areas and the depth of the water in several flooding stages. We examined flood scenarios in the event of a breach occurrence in a settlement’s dyke during a heavy river flooding to investigate the impact on the study area in terms of flooded surfaces, using two methods. We also took into account the 2010 Danube flood, relying on hydrological data (records at the Tulcea-port hydrometric station that produced daily data, and data provided by the Danube Delta National Institute for Research and Development, and the Romanian Waters National Administration). Using GIS, the processing of this data could be done much faster and easier, and in a much more accessible interface. The use of GIS is essential in flood scenario analysis; throughout the process, from its initiation to the interpretation phase. The GIS systems were used to facilitate the visualization of the study area, and to enter, refine, and process the hydrological data needed in the flood scenario analysis, as well as to interpret the results, and to present them as comprehensively as possible. In order to choose the most appropriate flood analysis model, it is necessary to have access to geographical information that can provide an overview of the components of the water circulation system [47]. High resolution digital terrain models are decisive for the precise output of flood stages and flooded surfaces. Table6 presents a comparative analysis of the analysis methods used in this work, taking into account the software options.

Table 6. Comparative analysis of the analysis methods used.

Dynamic Method Static Method Program Options HEC-RAS Global Mapper Modeling 1D Yes No Modeling 2D Yes No Data import/export functions Yes Yes Creating the hydraulic flow model Yes No Creating the flow surface Yes Yes Initial input complexity/limit conditioning High/medium Low/NA Various ways of presenting the results Yes Yes Interconnectivity with other software Yes Yes Introduction of riverbed sections Yes No Import/export GIS data Yes Yes Appreciation of facilities/price Yes Yes Determining the simulation time Yes No View depths in the flooded perimeter Yes No Introduction of the flow slope Yes No Point spacing computation Yes Yes Choosing interval computing Yes No Choosing mapping output interval Yes No Choosing hydrograph output interval Yes No

6. Conclusions In the Danube Delta, floods are hydrological events that can occur at any scale, and it has been noticed that they have appeared more often in the last decades. Flooding is influenced by the rate of the Danube water level rise. Depending on their magnitude, they proportionally affect a certain part of the Delta area, and especially the settlements along the main branches of the river. From this perspective, the analysis of two different scenarios considering the breaching of the dyke for localities along the Sulina branch of the Danube River was performed in the present work. Appl. Sci. 2020, 10, 8327 21 of 28

The study was carried out using a dynamic method of hydraulic modeling, and a static method of GIS analysis, to identify the flooding maps for different scenarios. An important conclusion was derived from the topo-bathymetric measurements. To achieve the main purpose of this study, it was important to develop a good DTM, by improving the LIDAR model with minor river bathymetry. The digital model of the minor river bed, coupled with the digital terrain model for the Danube Delta allowed the simulation of more flooding scenarios for the localities, and considering various flooding tests. Furthermore, it can be mentioned that the results of hydraulic modeling, in case of dynamic modeling, are directly influenced by the coefficient of roughness (Manning’s roughness coefficient), which in this study ranged between 0.025 and 0.03. According to Chow [48], these Manning values are specific to natural minor streams; clean, straight, full stage, no rifts, or deep pools. Furthermore, the results of the hydraulic model were validated by using real data. This revealed the effectiveness of the model in identifying areas prone to floods. The results showed that for the first breaking scenario of the dyke (the dynamic method) the flooded areas covered a smaller area than in the case of the second scenario (the static method), when the flooded zones covered a considerably larger area. The most significant differences were noticed for the localities Vulturu, Cris, an, and Sulina. The results obtained also revealed the flooded surface in percentage and the expected water depth in each locality studied. The increase of the Danube levels led to a gradual increase of the flooded surface inside the localities, in the case of a breach in the dyke. The accuracy of the model simulations depends to a large extent on the accuracy of the DTM, the hydrological and hydraulic structure of the model, and the characteristics of the soil. Finally, it can be concluded that floods caused by high water levels or by dam failure can be analyzed through a combination of 1D/2D techniques or through a GIS analysis. The emphasis was on comparison of flooded surfaces resulting from the two methods. The static method used with the help of Global Mapper software does not require so much data and knowledge from the operator as the dynamic method. The second method requires complex data and information for the elaboration of a hydraulic model in HEC-RAS for a 1D/2D simulation (field data, hydraulic parameters, flows, levels, types of graphical representation, networks, nodes, conditions of margin). The use of the static method in performing flood simulations does not require topo-bathymetric data like the dynamic method, which results in the fact that the static method is exempt from the time and effort allocated to perform field measurements. At the same time, the disadvantage of the static method is that the simulations cannot be calibrated with field data. The dynamic methodology has the advantage of introducing a much more complex input data set, including calibration data, which gives a higher confidence in the results obtained. From the comparison presented in Table6 and the results obtained from the simulations, it can be seen that both methods give similar results regarding the flooded area, the differences being outlined in the percentage of coverage of the flooded area in the localities. However, the dynamic method is much more realistic compared to the static method, due to the multiple advantages that the HEC-RAS program has, such as: multiple functions and options, choice of simulation period, facilities related to setting initial and boundary conditions, facilities related to the flow slope, etc. The approach of 1D/2D hydraulic modeling techniques is more realistic compared to the static method, taking into account the fact that the real flooded areas were determined for different scenarios of dyke breach with a duration of 24 h (the time of maintaining the breach), which could allow a real estimate of the material and human resources needed to intervene in an emergency. The approach with several scenarios allowed the testing of the water flow capacity in the settlements, determining to what extent the surfaces inside the settlements would be affected. The area targeted, located in the Danube Delta, presents an elevated danger of inundations. The novelty of this work is based on showing different complex techniques how can be used to assess flood extent by apply two different flood simulation methods. At the same time the used Appl. Sci. 2020, 10, 8327 22 of 28

Appl. Sci. 2020, 10, x FOR PEER REVIEW 22 of 28 methods represent a low to medium cost solution, by which flood maps can be generated in previously unassessedmethods areas. represent a low to medium cost solution, by which flood maps can be generated in Thepreviously purpose unassessed of the floodingareas. maps is to support decision making, the elaboration of flood managementThe plans, purpose public of the awareness, flooding maps or other is to activities support of decision general making, interest. the Furthermore, elaboration of the flood analysis performedmanagement is important, plans, public and awareness there is a, hugeor other potential activities forof general the development interest. Furthermore, of a robust the approachanalysis in otherperformed similar situations is important for,any and typethere of is area.a huge potential for the development of a robust approach in Lastother but similar not least,situations ideas for for any a newtype researchof area. work were derived from this paper, entailing the use of Last but not least, ideas for a new research work were derived from this paper, entailing the use the results in order to assess the possible flood risk and hazard for all the presented localities. In this of the results in order to assess the possible flood risk and hazard for all the presented localities. In case repeated in situ bathymetric measurements will be done, in order to assess the spatial-temporal this case repeated in situ bathymetric measurements will be done, in order to assess the spatial- differences,temporal and differences, to obtain and an updatedto obtain an hydrological updated hydrological model. model.

AuthorAuthor Contributions: Contributions:A.B. A processed.B. processed the the numerical numerical data, data, designed designed the the ﬁgures figures and and tables tables andand wrotewrote thethe ﬁrstfirst form of theform manuscript, of the manuscript, C.I. contributed C.I. contributed to the interpretationto the interpretation of the ofresults the results and and L.P.G. L.P.G supervised. supervised the the work.work. E E.R..R. and M.A. performedand M.A. performed the literature the literature review, correctedreview, corrected the manuscript the manuscript and acted and acted as corresponding as corresponding author. author. All All authors have readauthors and have agreed read toand the agreed published to the versionpublished of version the manuscript. of the manuscript.

Funding:FundiThisng: This work work was was supported supported by by the the project project “EXPERT”,“EXPERT”, financed ﬁnanced by by the the Romanian Romanian Ministry Ministry of Research of Research and Innovation,and Innovation, Contract Contract No. No. 14PFE 14PFE/17.10.2018./17.10.2018.

Acknowledgments:Acknowledgments:This This work work was was supported supported by by the the projectproject “Excellence, performance performance and and competitiveness competitiveness in in the Research,the Research, Development Development and and Innovation Innovation activities activities at “Dunarea de de Jos” Jos” University University of Galati”, of Galati”, acronym acronym “EXPERT”.“EXPERT This”. This work work was was financed financed by the by Romanian the Romanian Ministry of Ministry Researchof and Research Innovation and in the Innovation framework of in the framework of Program 1—Development of the national research and development system, Sub-program Program 1—Development of the national research and development system, Sub-program 1.2—Institutional 1.2—Institutional Performance—Projects for financing excellence in Research, Development and Innovation, Performance—Projects for financing excellence in Research, Development and Innovation, Contract No. Contract No. 14PFE/17.10.2018. The authors would like also to express their gratitude to the reviewers for their valuable14PFE/17.10.2018. suggestions and The observations authors would that like helped also to in express improving their the gratitude present to work. the reviewers for their valuable suggestions and observations that helped in improving the present work. Conflicts of Interest: The authors declare no conflict of interest. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Appendix A

FigureFigure A1. A1.Graphs Graphs with with monthly monthly distribution distribution of of the minimum and and maximum maximum water water levels levels of the of the DanubeDanube at the at Tulceathe Tulcea hydrometric hydrometric station station in in 2003 2003 andand 2010. ( (aa) )M Monthlyonthly frequency frequency of the of theminimum minimum waterwater levels; levels; (b) monthly (b) monthly frequency frequency of the of maximum the maximum water water levels; levels; (c,e ) (c graphs,e) graphs with with normalized normalized values corresponding to the monthly frequency of the minimum water levels; (d,f) graphs with normalized values corresponding to the monthly frequency of the maximum water levels. Appl. Sci. 2020, 10, x FOR PEER REVIEW 23 of 28

values corresponding to the monthly frequency of the minimum water levels; (d,f) graphs with Appl. Sci. 2020, 10, 8327 23 of 28 normalized values corresponding to the monthly frequency of the maximum water levels.

Figure A2. The digital terrain model for the Danube Delta superimposed on the orthophotomaps Figure A2. The digital terrain model for the Danube Delta superimposed on the orthophotomaps (the (the red dots indicate the settlements located along the Sulina branch. red dots indicate the settlements located along the Sulina branch.

Appl. Sci. 2020, 10, 8327 24 of 28 Appl. Sci. 2020, 10, x FOR PEER REVIEW 24 of 28

Figure A3. BathymetricBathymetric cross cross-section-section profiles proﬁles (magenta (magenta lines) lines) and and bank bank points points (yellow (yellow dots), dots), measured measured for for S1 S1(a), (a S2), S2(b), (b and), and S3 S3(c), ( csections), sections of the of the study study area. area. The The third third line linesubfigures subﬁgures (d–f) ( drepresents–f) represents the combined the combined 3D model 3D model obtained obtained from L fromIDAR LIDAR and bathymetric and bathymetric data for data all three for all section threes sectionsof the study of the area. study area.

Appl. Sci. 2020, 10, 8327 25 of 28

Appl. Sci. 2020, 10, x FOR PEER REVIEW 25 of 28 14′

LONGITUDINAL SECTION S1 45

12′

Depth (m) Depth 11′ 3 Km 45 (a) (a1)

28 42′ 28 44′ 28 47′ 28 51 ′

Distance from start of transect (km) 12′

S2 LONGITUDINAL SECTION S2

11′

Depth (m) Depth 09′

3 Km 45 (b) (b1)

28 52′ 28 53′ 28 55′ 28 56 ′

Distance from start of transect (km)

11′

45 LONGITUDINAL SECTION S3

8.90 km

09′

45 Depth (m) Depth

S3 08′

3 Km 45 (c) (c1)

29 35′ 29 37′ 29 39′ 29 42′ Distance from start of transect (km) FigureFigure A4. Longitudinal A4. Longitudinal profile proﬁle of the of main the mainchannel channel of all three of all section three sectionss from study from area study: (a– area:c)—the (a– planec)—the view plane of longitudinal view of longitudinal river centerline river centerline of S1, S2 and of S1, S3 S2cross and-section S3 path;cross-section (a1–c1)—section path; view (a1–c1 for)—section the depth view profiles for the of centerline depth proﬁles at S1, of S2 centerline and S3 river at S1, part S2. and S3 river part.

Appl. Sci. 2020, 10, 8327 26 of 28 Appl. Sci. 2020, 10, x FOR PEER REVIEW 26 of 28

Rating curve of Tulcea hydrometric station from 2010 year

Danube River

(cm) H

3 Q (m /s) Figure A5. Rating curve values at the Tulcea gauge station during the 2010 ﬂoodflood year.year.

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