Authors:
Gábor Murányi Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary

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László Koncsos Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary

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Abstract

The floodplains of the Tisza River, stretching across the eastern part of Hungary, are often affected by riverine and inland excess water flooding and draught. This paper investigates a possible solution to this problem utilizing the water retention capabilities of old floodplains. In this study, the effect of the position of the inlet structures of a floodplain, near Csongrád town, was examined with HEC-RAS 1D-2D coupled model. Based on the results, the rules of the deep floodplain selection were determined. On the extended model, the possibilities of a deep floodplain storage area chain have been explored. According to the estimate, more than 2.36 km3 potential storage capacity is available along the Hungarian section of the Tisza River.

Abstract

The floodplains of the Tisza River, stretching across the eastern part of Hungary, are often affected by riverine and inland excess water flooding and draught. This paper investigates a possible solution to this problem utilizing the water retention capabilities of old floodplains. In this study, the effect of the position of the inlet structures of a floodplain, near Csongrád town, was examined with HEC-RAS 1D-2D coupled model. Based on the results, the rules of the deep floodplain selection were determined. On the extended model, the possibilities of a deep floodplain storage area chain have been explored. According to the estimate, more than 2.36 km3 potential storage capacity is available along the Hungarian section of the Tisza River.

1 Introduction

The effects of climate change can be already observed on the Great Hungarian Plain. Groundwater table have been dropped by several meters in the last decades at certain zones [1]. Although the total volume of annual precipitation has not been changed significantly, it has been becoming more variable in space and time. It is well known that the uncertainty of the prognosis generated by climate change forecast models is high. Nevertheless, the forecasts show that the evaporation, the possibility of the flash floods and the droughts will increase and the decreasing recharge of groundwater is foreseeable [2]. The changing climate, making extreme meteorological phenomenon more likely, will most likely strengthen the adverse effects of these phenomena. It is therefore becomes necessary to save available water resources. To achieve this, as a prerequisite, the review of the existing approaches of water management is needed.

The flood waves between 1998 and 2001 generated the highest water levels from year to year without significant growth in measured discharges. A series of flood waves between 1998 and 2001 resulted in increasing flood levels year by year. The ever heightening of the levee's crests is not a sustainable solution. Due to the vast sediment deposition across the flood channels, the further rise of the flood levels is expected. The sedimentation ratio in the Middle-Tisza Section is about 1.1 cm year−1, and in the Lower-Tisza section 0.8 cm year−1 [3, 4].

The solution from the official Hungarian water management was the ‘Further Development of Vásárhelyi's Plan’ (FDVP) [5]. Pál Vásárhelyi was the designer of the Tisza River regulation. The concept of the FDVP was to build storage areas on the selected deep floodplains and launch a new, integrated water management [5]. Till today not all the planned storage areas have been built, and existing ones are only be used in critical cases to reduce flood peak.

The aim of the present study was to investigate the opportunities of a nature-based flood management solution in the Tisza River Valley, Hungary. Based on previous investigations, many deep floodplain areas have been identified along the Tisza River. These areas were formerly integrated parts of the Tisza River's floodplain. Since the river regulation works and levee constructions of the 19th century, the deep floodplains have been cut from the river by the flood levees and the direct hydraulic contact with the river has been lost. It seems to be logical that recovered and reattached deep floodplains can be used as storage areas [6–8]. To reach this, there is no need for the destruction of the dikes. The construction of sluice gates could provide the connection between the storage area and the river. The inflow and the outflow can be securely managed. The formerly approximated total capacity of the storage areas is 2.5 cubic kilometer [9]. A more precise estimation is given in this paper. This nature-based solution offers multiple positive effects: on one hand, the flood risk mitigation, while on the other, it can be an effective tool to mitigate drought and excess water flood risk. The feasibility is caused by the peculiarity of the Tisza Valley. As it is pointed out in the National Water Strategy (‘Jenő Kvassay Plan’) [10], while in the upper section of the Tisza Valley, riverine and excess water flood risk are more common, in the lower section, all three risk factors occur together. The essential of the deep floodplain storage method is the ‘calm’ water withdrawal. The filling of the reservoirs goes on simultaneously with the rising water level in the river channel. The sluice gates close when the water reaches the allowable level in the reservoir. Thus, water flows do not damage the vegetation [11].

The previous phase of the research showed that natural deep floodplains may function properly as a flood risk mitigation system in a different way than the flood peak reduction method [12]. The goal in the current phase is to examine the effect of the position of flood control structures on the flood levels and identify the possible areas for the deep floodplain storage system. Utilizing deep floodplain storage capacity is only the first step in a new planned, integrated, nature-based water management strategy subject to research. The further functions of the reservoirs are reducing the risk of flooding, act as Managed Aquifer Recharge (MAR) areas and supply storage water background reservoirs far from the river. The precondition of implementation of aforesaid elements in the current water management is the changes of the land-use on the affected areas. The prevailing land-use nowadays on these areas are crop fields. As natural reservoirs, grasslands, wetlands, pastures, and floodplain forests are the recommended land uses. With this land-use change, drought and inland excess water risk can be mitigated. Furthermore, restoring the water-related ecosystem can increase biodiversity, which has a positive impact on ecosystem services. As a MAR tool, the trend-like decline in the groundwater level is expected to slow down, moreover, the groundwater level is expected to increase. Approximation of these effects will be the topic of the next research phase, however, based on previous research, positive effects are expected [13–15].

2 Methodology

According to a previous comprehensive analysis [12], flood risk could be efficiently mitigated on deep floodplains, which was regularly covered by water before the river regulation works. The aim is to analyze the effect of the position of the inlet structures and identify further deep floodplain areas suitable for flood mitigation, preparing the model to the next research phase.

The following data and information were available for our investigations:

  1. 25 × 25 m cells Digital Elevation Model (DEM) for Hungary, Google Maps;
  2. Cross section data of the Tisza River along the Hungarian longitudinal section;
  3. Time series of the floods between 1998–2001;
  4. Data about the structure and the operating rules of Tiszalök and Kisköre hydropower plants;
  5. Historical map database [16] and historical ethnographical and land use descriptions [17, 18].

As a first step a comprehensive comparative study was carried out analyzing the possible technical solutions. The combined 1D/2D model has two parts. The 1D Saint-Venant equations were used for the Tisza River. The detailed examination of the inundation process on storage area was carried out with the 2D solver that gives the Diffusive wave approximation of the Shallow Water (DSW) equations using the Reynolds-Averaged Navier-Stokes equation (RANS). The connection between these model components were provided by a sluice gate [19]. For optimization of the reservoir operation the 2D model was replaced by a simplified 0D storage model, which results in targeted operation of the sluices. Based on the DEM an elevation-volume curve was used to describe the reservoir storage capacity [20]. In the frame of the first simulations it was assumed there will be a two-structure system. An inlet structure on the upstream site and one outlet structure nearby the downstream site on a deeper point were assumed. The operation regime of these structures is equivalent to the current reservoir operation of that of the FDVP reservoirs. In the second case, based on the descriptions of the historical land use, just one multifunctional structure was used nearby the floodplain embankment on the deepest point. This alternative was used to provide a simulation of the ancient traditional floodplain management methodology. The essential of this approach is filling up the floodplain from the deepest point because it results a moderate inundation velocity and the sedimentation will happen on the deepest point. Meanwhile the drainage of the storage the outflow will work against the sedimentation. With the help of this approach the decrease of the maintenance work was targeted [11, 17].

In the first phase, the 1D Tisza River model was applied between Kisköre hydropower plant the river mouth at the Danube, south of Titel. For the calibration procedure the river discharge/stage data of year 1999 was used, while river data of 2000 have been used for validation purpose. After the hydrodynamic simulations, the model was extended for the whole country. That means, the model's new upstream boundary is at Tiszabecs, where the Tisza River enters Hungary. With using the US Army Corps of Engineers Hydrologic Engineering Center River Analysis System's (HEC-RAS) mapper module, the morphological model of the riverbed was exported using linear interpolation. The Tisza River's bed data were integrated into the DEM using a separate Geographic Information System (GIS) software. The DEM covers the area of Hungary, therefore where the Tisza River separates Hungary from the neighboring countries, the river bed data was missing. Based on the Google maps, the satellite pictures, and the original cross section data the missing parts were replaced with a synthesized triangulated network. Thereafter the 1D schematic for the extended Tisza model was drawn. The cross sections with the new composite terrain data was inspected and overwritten. Based on the new model layout, potential deep floodplain reservoirs have been explored. Existing FDVP storages were also taken into consideration. The Bodrogzug area act like a reservoir, therefore it has been built into the model as a storage area, with open connection to the river. It is an important part of the Tisza River Valley, but it is not a part of the deep floodplain reservoir system, because the inflows and outflows are uncontrolled.

The rules for selecting the floodplains were the following:

  1. The permissible normal water level by operation can cause 1.5–2 m deep average inundation;
  2. By this average depth, the storage capacity must be greater than 20 million m3;
  3. The boundary edge of the reservoir follows the alignment of the existing infrastructures, estate borders or the high banks;
  4. If the reservoir area overlaps with villages or bigger farm complexes, the affected area will be protected with ring levee where it is necessary;
  5. The multifunctional structure can be used.

3 Model analysis and calculation results

3.1 The effects of the structure position

The study area is located on the right side of the Tisza River, south of Csongrád, east of Felgyő. The elevation versus volume relationship gives the storage capacity curve shown in Fig. 1. Based on the selection rules of the deep floodplains, the allowable normal water level is 81 Meters Above Sea Level (MASL). For that level, the capacity of the reservoir is 51.43 million m3.

Fig. 1.
Fig. 1.

Reservoir capacity curve

Citation: Pollack Periodica 17, 3; 10.1556/606.2022.00456

The structure for the outflow control in both cases was a 2 × 10 m wide sluice gate, with the bottom sill on 78.1 MASL. The operation inundation level was at 81 MASL. For the 2D inundation mapping, the sluice gate opening time series for the calibration flood of year 2000 were used. The different approach of the 0D and 2D simulation can cause differences in the emerging water levels. First, the inundation spreads from the northeasterly part of the deep floodplain. The maximal water surface was 80.90 MASL in this case. The arrival time in the upper quarter of the storage area was under 12 h. The inundation propagation is strongly influenced by the existing drainage or irrigation canals. After 24 h approximately the half of the studied area was flooded. It took a little over 96 h to reach the allowable water level, but after 48 h the whole area is under water-cover. The results can be studied in Fig. 2. The maximal discharge of the sluice gate was 198.95 m3 s−1. At the water gauge near Szeged town, the maximal mitigation of the flood level was 12 cm peaking on 03/17/2000.

Fig. 2.
Fig. 2.

Arrival time with two structure system, inflow from the northeasterly part

Citation: Pollack Periodica 17, 3; 10.1556/606.2022.00456

In the second case, a multifunctional structure was examined, positioned to the deepest point nearby the floodplain dike. The reached maximal water level was 80.95 MASL. In the first 24 h the deeper trenches were inundated, then in the next 24 h the upper trenches. After filling the canals and the nearby deeper plains, it took more than 180 h to flood the entire storage area (see Fig. 3). This means 3.5 days more charging time than the first version. The maximal measurable effect on the Szeged water gauge was 13 cm on 03/17/2000. The maximal discharge on the sluice gate was 153.30 m3 s−1.

Fig. 3.
Fig. 3.

Arrival time with multifunctional structure system, inflow by the deepest point nearby the embankment

Citation: Pollack Periodica 17, 3; 10.1556/606.2022.00456

Although the charging time is almost double of the two-structure system, from the viewpoint of the eco-friendly inundation the one multifunctional structure system is preferable. The highest velocity occurs in the existing canals. The prevailing practice of water management drains out the deep floodplains on the deepest points to handle the groundwater flood. The advantage of a system similar to the traditional one is that, in theory, the existing drainage channels can be used as multifunctional channels. Furthermore, investment and operating costs can be reduced if only one structure is built.

The arrival times described above do not include the time needed to empty the reservoirs and the retention time. The required hydraulic retention time depends on the specific flood wave and the intended use of the stored water. Theoretically, implementing reservoir elements of the complex system, from the deep floodplains the water flows further away and will be used firstly to aquifer recharge. From certain storage areas gravity transfer is not possible a part of the stored water flows back to the river, another part seeps into the soil and/or evaporates.

3.2 Review of the potential deep floodplain reservoirs

The hydraulic analysis showed that the integrated multifunctional structure design is a technically appropriate solution. Keeping in mind the rules of reservoir designation and striving for a multifunctional structure design, a total of 36 deep floodplain areas were explored. Six from that is an existing FDVP reservoir. To keep model uniformity, the official data of the FDVP storages were not used. All the elevation versus volume curves were generated using the composite DEM data generated for this study. The selected storage areas meet the requirements, except the storage area No020. That is a possible sample area for the future field experiments. It is located north of Tiszaroff town. On the historical land use maps the name of this area is ‘Tó lapos’ in Hungarian, which means plain lake or shallow lake. The potential deep floodplain storage areas of the Tisza Valley are summarized in Table 1.

Table 1.

Deep floodplain storage areas in the Tisza River Valley

ID Name RKM Area [km2] Max. level [MASL] Volume [106 m3]
001 Milota 731+100 23.05 118.00 61.83
002 Beregi VTT 706+850 58.63 109.20 32.82
003 Kisar 690+000 75.95 110.80 65.94
004 Gergelyiugornya 680+740 39.96 109.40 61.90
005 Cigánd VTT 597+700 25.28 97.80 57.79
006 Dombrád 569+000 163.85 95.80 88.79
007 Tiszakarád 566+400 40.33 96.20 65.30
008 Tiszaladány 512+000 58.64 94.60 60.95
009 Taktakenéz 503+500 27.87 93.70 38.54
010 Tiszalúc 493+500 38.55 94.40 73.76
011 Tiszagyulaháza 488+000 55.24 92.00 42.07
012 Polgár 468+400 25.87 91.90 35.09
013 Tiszacsege 457+000 54.36 90.90 48.59
014 Tiszadorogma 442+000 203.79 90.50 67.45
015 Tiszafüred 434+300 71.31 89.15 65.62
016 Sarud 414+000 26.22 89.50 57.72
017 Tiszaderzs 411+700 68.28 88.60 138.83
018 Tiszanána 404+500 66.54 88.65 98.70
019 Nagykunsági VTT 400+600 40.34 88.00 91.95
020 Tiszaroff sample 391+000 5.58 86.00 7.31
021 Hanyi-Tiszasülyi VTT 388+900 55.73 86.00 27.80
022 Tiszaroffi VTT 370+100 23.37 87.50 45.63
023 Nagykörű 354+300 49.24 85.60 42.16
024 Tiszabő 362+000 31.28 87.50 87.12
025 Tiszapüspöki 351+400 68.01 87.50 197.76
026 Tószeg 321+500 44.62 87.70 35.31
027 Tiszaföldvár 294+500 45.26 83.80 58.46
028 Tiszakécske 275+500 29.55 83.50 51.26
029 Tiszasas 249+600 73.64 82.50 126.47
030 Szentes 238+000 70.39 81.50 62.71
031 Csongrád 228+800 29.91 81.00 51.43
032 Mindszent 218+000 57.35 79.70 31.52
033 Ópusztaszer 204+700 70.90 79.60 85.15
034 Dóc 196+700 49.07 79.50 71.30
035 Mártély 204+000 20.30 81.00 31.97
036 Hódmezővásárhely 183+700 158.30 78.50 93.76
Sum 2,360.75

In Table 1, the reservoirs are listed from upstream to downstream. The positions of these storage areas are illustrated in Fig. 4. The sum of the selected area is 2,046.56 km2. The total storage capacity is 2.36 km3. This is 1.48 times larger than Hungary's largest lake, Lake Balaton (approximately 1.9 km3).

Fig. 4.
Fig. 4.

Deep floodplain storage areas in the Tisza River Valley

Citation: Pollack Periodica 17, 3; 10.1556/606.2022.00456

4 Discussion and conclusion

The analysis performed in the present study proved that both examined systems for the deep floodplain storage method, the separated inlet and outlet structure system and the multifunctional integrated structure system are suitable solution for the reservoir operation. The essential of the method has been validated, ergo the water withdrawal started in the beginning of the flood. The water level in the reservoir changed gradually, water fluxes did not exceed 200 m3 s−1. Moreover, with the multifunctional structure it was under 160 m3 s−1. The maxima of the flood level mitigation at the Szeged water gauge were quasi the same. The one structure system is also advocated by the presumably higher cost efficiency.

The model of the Tisza River was extended to the upstream end of the Hungarian river section at Tiszabecs. For that the digital elevation model was further developed, which provided an opportunity to analyze all the possible locations of deep floodplains. With the use of the selection rules and results of previous research, 36 potential deep floodplain storage areas were explored. The sum volume of these reservoirs is 2.36 km3. It is noteworthy that this capacity is not an exact number. It depends on the applied boundaries of the storage areas and the allowable normal water level. That can be the explanation of the differences in results between this research and the previous works.

The former work pointed out that the deep floodplain storage method can be a nature-based alternative solution for the flood, drought, and inland excess water flood risk mitigation but only in a storage chain system. This paper has shown that the necessary storage capacities are available in the Tisza Valley. The next goal is the optimization of the deep floodplain storage chain operation, after the calibration and validation of the extended Tisza model.

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  • [1]

    G. Mezősi , “Physical geography of the great Hungarian plain,” in The Physical Geography of Hungary. Geography of the Physical Environment, Springer, 2017, pp. 195229.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [2]

    B. Nováky , “Climate change and its effects(in Hungarian), in Hungary's Water Management: Situation and Strategic Plans (in Hungarian). L. Somlyódy , Ed., Budapest: Hungarian Academy of Sciences, 2011, pp. 85102.

    • Search Google Scholar
    • Export Citation
  • [3]

    F. Schweitzer , I. Nagy , and L. Alföldi , “Relationship between the formation of point bars and natural levees and flood bed sedimentation along the middle stretches of Tisza River(in Hungarian), Földrajzi Értesítő, vol. 51, no. 3–4, pp. 257278, 2002.

    • Search Google Scholar
    • Export Citation
  • [4]

    F. Schweitzer , “Decision-making constraint in the Hungarion flood management(in Hungarian), in Studies from the Fields of Geomorphology, Geochronology, Hydrography and Mars-research (in Hungarian). F. Schweitzer , Ed., Budapest: MTA CSFK Földrajztudományi Intézet, 2017, pp. 103135.

    • Search Google Scholar
    • Export Citation
  • [5]

    Concept of integrated territorial development, rural development and environmental development on the areas along the Tisza River (in Hungarian), VÁTI Hungarian Public Nonprofit Company for Regional Development and Town Planning, Budapest, 2004. [Online]. Available: http://www.terport.hu/webfm_send/87. Accessed: May 27, 2021.

    • Search Google Scholar
    • Export Citation
  • [6]

    L. Koncsos , “Flood regulation of the Tisza in the Carpathian Basin(in Hungarian), Magyar Természetvédők Szövetsége, Budapest, pp. 431, 2006.

    • Search Google Scholar
    • Export Citation
  • [7]

    Z. Derts and L. Koncsos , “Ecosystem services and land use zonation in the Hungarian Tisza deep floodplains,” Pollack Period., vol. 7, no. 3, pp. 7990, 2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [8]

    Z. Derts , L. Koncsos , and Z. Simonffy , “The flood protection situation of the Tisza in Hungary: current practice and alternative possibilities,” (in Hungarian), World Wide Fund for Nature – Under review, Budapest, 2018.

    • Search Google Scholar
    • Export Citation
  • [9]

    Z. Derts and L. Koncsos , “Flood risk mitigation in the Tisza Valley by deep floodplain reservoirs: The effect on the land use,” J. Environ. Sci. Eng. B1, pp. 3440, 2012.

    • Search Google Scholar
    • Export Citation
  • [10]

    National Water Strategy – Jenő Kvassay Plan (in Hungarian), General Directorate of Water Management, Budapest, 2017. [Online]. Available: https://www.vizugy.hu/vizstrategia/documents/997966DE-9F6F-4624-91C5-3336153778D9/Nemzeti-Vizstrategia.pdf. Accessed: May 27, 2021.

    • Search Google Scholar
    • Export Citation
  • [11]

    G. Gábris , G. Timár , A. Somhegyi , and I. Nagy , “Flood storage or floodplain management along the Tisza(in Hungarian), in 2nd Hungarian Geographical Conference (in Hungarian), Szeged, Hungary, Sep. 4–5, 2004, pp. 118.

    • Search Google Scholar
    • Export Citation
  • [12]

    G. Murányi and L. Koncsos , “Examination of a nature-based flood control solution in the Tisza River Valley nearby Csongrád town with HEC-RAS 1D-2D coupled model(in Hungarian), Hidrológiai Közlöny - under rewiev, 2021.

    • Search Google Scholar
    • Export Citation
  • [13]

    J. V. Ward , K. Tockner , U. Uehlinger , and F. Malard , “Understanding natural patterns and processes in river corridors as the basis for effective river restoration,” Regulated Rivers: Res. Manage., vol. 17, no. 6, pp. 311323, 2001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [14]

    K. Tockner and J. A. Stanford , “Riverine flood plains: present state and future trends,” Environ. Conserv., vol. 29, no. 3, pp. 308330, 2002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [15]

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Senior editors

Editor(s)-in-Chief: Iványi, Amália

Editor(s)-in-Chief: Iványi, Péter

 

Scientific Secretary

Miklós M. Iványi

Editorial Board

  • Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Jeno Balogh (Department of Civil Engineering Technology, Metropolitan State University of Denver, Denver, Colorado, USA)
  • Radu Bancila (Department of Geotechnical Engineering and Terrestrial Communications Ways, Faculty of Civil Engineering and Architecture, “Politehnica” University Timisoara, Romania)
  • Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
  • Oszkar Biro (Graz University of Technology, Institute of Fundamentals and Theory in Electrical Engineering, Austria)
  • Ágnes Borsos (Institute of Architecture, Department of Interior, Applied and Creative Design, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Matteo Bruggi (Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy)
  • Petra Bujňáková (Department of Structures and Bridges, Faculty of Civil Engineering, University of Žilina, Slovakia)
  • Anikó Borbála Csébfalvi (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Mirjana S. Devetaković (Faculty of Architecture, University of Belgrade, Serbia)
  • Szabolcs Fischer (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architerture, Civil Engineering and Transport Sciences Széchenyi István University, Győr, Hungary)
  • Radomir Folic (Department of Civil Engineering, Faculty of Technical Sciences, University of Novi Sad Serbia)
  • Jana Frankovská (Department of Geotechnics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • János Gyergyák (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Kay Hameyer (Chair in Electromagnetic Energy Conversion, Institute of Electrical Machines, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany)
  • Elena Helerea (Dept. of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Romania)
  • Ákos Hutter (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technolgy, University of Pécs, Hungary)
  • Károly Jármai (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Teuta Jashari-Kajtazi (Department of Architecture, Faculty of Civil Engineering and Architecture, University of Prishtina, Kosovo)
  • Róbert Kersner (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Rita Kiss  (Biomechanical Cooperation Center, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary)
  • István Kistelegdi  (Department of Building Structures and Energy Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Stanislav Kmeť (President of University Science Park TECHNICOM, Technical University of Kosice, Slovakia)
  • Imre Kocsis  (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
  • László T. Kóczy (Department of Information Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, University of Győr, Hungary)
  • Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
  • György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
  • Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
  • Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
  • János Lógó  (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
  • Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
  • Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
  • Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
  • Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
  • Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
  • Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
  • Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
  • Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
  • András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

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2023  
Scopus  
CiteScore 1.5
CiteScore rank Q3 (Civil and Structural Engineering)
SNIP 0.849
Scimago  
SJR index 0.288
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Pollack Periodica
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2023  
Scopus  
CiteScore 1.5
CiteScore rank Q3 (Civil and Structural Engineering)
SNIP 0.849
Scimago  
SJR index 0.288
SJR Q rank Q3

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