Advances in Environmental Sciences, Development and Chemistry

Catastrophic flood scenario between Kienstock and Nagymaros using NLN model

V. Bacova Mitkova, P. Pekarova, J. Pekar

and rural land use. Wu in [3] investigated potential impact of Abstract—Hydraulic models used in hydrology are demanding climate change on flood risk for the city of Dayton, which lies large amounts of the input data. Therefore, utilization of the at the outlet of the Upper Great Miami River Watershed (Ohio, hydrological models of the wave transformation is one of the USA). He used a statistical model based on regression and alternative solutions for operative real time flood forecasting. This frequency analysis of random variables to simulate annual paper presents application of the simple nonlinear river model NLN Danube for the forecasting of the flood event occurred on the Danube mean and peak streamflow from precipitation input. River in June 2013. The model NLNDanube is the hydrologic model Hydrological model based on the CSC and kinematic wave aimed on the simulation of discharges in open channels. NLN model functions using gaugeadjusted radar rainfall data was applied simulates flood wave transformation in six river sections: Ybbs– for flash flood prediction in [4]. Simulation of the June 11, Kienstock–Devin/–Medvedov–Iza–Sturovo–Nagymaros. 2010, flood along the Little Missouri River, using a hydrologic The second part of this paper presents the simulation of the scenario model coupled to a hydraulic model was investigated in [5]. catastrophic flood for the today river conditions, based on the historical Danube flood from the year 1501. This flood was the Monitoring and evaluation of extreme hydrological highest flood described on the Upper Danube River basin during the phenomena through the different programs, projects and last 600 years. models are also highly actual for the second longest river in Europe – the Danube River – or its tributaries. For example, Keywords — catastrophic flood scenario, Danube River, the floods mapping on the Danube River using radar imaging nonlinear river routing model. SAR (Synthetic Aperture Radar) was reported in [6]. Pekarova et al. in [7] presented the history of floods and extreme flood I. INTRODUCTION frequency analysis of the upper Danube River at Bratislava. A RACTICAL applications of mathematical models and hydrological modeling framework applied within operational Pmathematical methods constantly increase especially in a flood forecasting systems on three Danube tributary basins number of important areas such as hydrology (e.g. flood (Traisen, Salzach and Enns) is presented in [8]. Dankers et al. forecasts in real time, flood protection, planning and design of in [9] dealt with simulation of flood risks for the Upper hydraulic structures, simulation of flood waves). Danube using the hydrologic model LISFLOOD. In [10] Determination of the flood hazard is an important aspect and Bohm & Wetzel analyzed historical floods on the rivers difficult task for hydrologic practice. and . Szolgay [11] used multilinear discrete cascade This development we can see in the growing number of model for river flow routing and real time forecasting in river publications, projects and mathematical models focused on reaches with variable speed. In [12] the model KLNMULTI hydrological modeling and forecasting. For example in the last was calibrated and used for modeling of several historical years Smith [1] applied databased mechanistic (DBM) models flood waves on the Danube River under present hydraulic to forecast flash floods in a small Alpine catchment. Kjeldsen conditions. Blaskovicova et al. in [13] evaluated trends of the [2] tested the effect of urban land cover on catchment flood changes of the average annual and maximum annual response using a lumped rainfall–runoff model, and compared discharges on the Danube River in Bratislava gauging station. flood events from selected UK catchments with mixed urban The Danube River was and still is a symbol of strategic importance and trade. River regime conditions of the Danube River have been continually changing. These changes result This work was supported in part by the Slovak Research and Development Agency under the contract No. APVV01510 and by the MVTS „Flood from the natural processes (erosion, sedimentation, vegetation regime of rivers in the Danube river basin“, and it results from the project cover) or anthropogenic activities (modification of the implementation of the “Centre of excellence for integrated flood protection of riverbank, construction of dykes and hydro–power stations). It land” (ITMS 26240120004) supported by the Research & Development has significant impact upon the transformation of flood waves Operational Programme funded by the ERDF. V. Bacova Mitkova is with Institute of Hydrology Slovak Academy of in the river channel. Sciences, Bratislava, Slovakia, (email: [email protected]). On the basis of the development mentioned above the short P. Pekarova is with Institute of Hydrology Slovak Academy of Sciences, term forecasting of the flows becomes more demanded. From Racianska 75, 831 02 Bratislava, Slovakia, (phone: +4212 44259311, Fax: +4212 44259311, email: [email protected]). this reason it is necessary to use and to deal with new methods J. Pekar is with the Faculty of Mathematics, Physics and Informatics, and procedures that better reflect changes in hydrological river Comenius University, Bratislava, Slovakia (email: [email protected]).

ISBN: 978-1-61804-239-2 393 Advances in Environmental Sciences, Development and Chemistry

conditions. This process cannot be considered closed. ).( −=− VVTQP ii ++ 11 +1 ii , (2) Forecasting models and methods have to be constantly updated to the latest conditions and current situation in the basin. For where: this purpose simple river model NLNDanube [14], [15] was Pi+1,Qi+1 the average i/o of the interval i+1; constructed. Development of conceptual nonlinear reservoir Vi+1,Vi storage at the interval i+1 and i. cascade models was one of several approaches to incorporate nonlinearity into hydrological routing models (see [16][20]). From equations (1) and (2) we receive: The objective of this paper is to present (on the example of extreme flood situation on the Danube River) results needed /1 EX 1/ EX for flood protection obtained using a relatively simple tool of Qi+1 − Qi + ii +11 ).( − TQP = engineering hydrology. Firstly, short description of the non B /1 EX (3) linear river model NLNDanube is presented. Secondly, the model NLNDanube was used for forecasting of the flood The equation (3) defines the nonlinear function f of one event occurred in June 2013, calibrated on the flood event on unknown Qi+1, the Danube River in August 2002. Forecasting of the June

2013 flood discharges by model NLNDanube was done on the Q /1 EX − Q1/ EX Danube River reach Kienstock–Sturovo. Finally the simulation ).()( −= TQPQf − i+1 i i+1 + ii +11 B /1 EX of transformation of the potentially catastrophic flood by the , (4) model NLNDanube for present river regime conditions was which is searched by linearisation (Newton) method done. f (Q k )( ) II. NONLINEAR ROUTING MODEL NLN–DANUBE k+ )1( = QQ k)( − i+1 i+1 i+1 ′ Qf k )( ()i+1 , (5) A. Model description

Model NLNDanube [15] goes out from model NONLIN by what gives in our case the iteration formula: Svoboda (1993, 2000). Model of each section of the simulated system is based upon the concept of a series of equal non k )( − − QTQP /1)( EXk − )()().( EX .BQ − /1/1 EX linear reservoirs, thus belonging to the category of k+ )1( k )( + ii +11 [ i+1 i ] i+1 QQ i+1 += −EXk EX − /1/)1()( EX −1 hydrological conceptual nonlinear models. Model input (P) + QT i+1 ..)( EXB represents the input into the first reservoir of the cascade . (6) (Figure 1), its output is the input into the second one in series, The parameters of the transformation curve shape are etc., and the output from the last reservoir is the output (Q) expressed by ratio parameter B, from the model of the section. EX  N.T  P Q B =    BK  , (7)

V1 V2 VN where: N number of storages in one section of the model; BK ''time constant'' of an equivalent linear system.

Fig. 1 Scheme of the NLNDanube model The iteration process (6) is performed with accuracy of 0.001. Parameters of the model calibration are: Movement of the wave through reservoir is defined by BK time constant of the equivalent linear system [hrs]; discharge (Q) and by volume of reservoir (V) as: T length of the time step [hrs]; QC corresponds to the maximum capacity of the EX 3 1 = .VBQ (1) river channel (flow, when water enters the inundation) [m s ]; where: EX the nonlinearity parameter, dimensionless; Q reservoir output; N number of reservoirs in series, dimensionless; V volume of reservoir [m3]; NU, NL tributaries in section (yes=1, no=0). EX the nonlinearity parameter; B. Model calibration B the proportionality parameter. Model NLN–Danube was calibrated on set of the summer The flood wave propagation is modeled in equidistant floods occurred during 1991– March 2002 for river reach discrete time steps 0, 1, 2, … m. The difference between two Kienstock–Nagymaros (Figure 2). The up stream water steps is given by parameter T. In time steps i and i+1, for gauging station Kienstock was chosen because it is located at a known input Pi+1 and output Qi, the unknown output Qi+1 is sufficient distance from Bratislava and gives a fair forecast determined from the continuity equation within the time lead time. interval i+1 of the length T as:

ISBN: 978-1-61804-239-2 394 Advances in Environmental Sciences, Development and Chemistry

11500 Devin/Bratisl. Qf Berg 10000 Devin/Bratisl. Qm Bratislava ] 8500 1 s Kienstock Nagymaros 3 7000

e 5500

b u

Q [m Q 4000 n Ceatal Izmail a 2500 D 1000 05Aug02 10Aug02 15Aug02 20Aug02

10000 Medvedov Qf 8500 Medvedov Qm ]

1 7000 s Fig. 2 The gauging stations KienstockBratislavaNagymaros in the 3 5500 4000

Danube River basin Q[m 2500 1000 The hourly discharge data of the flood were available from 05Aug02 10Aug02 15Aug02 20Aug02 the Slovak Hydrometeorological Institute (SHMI) – Bratislava, gauge Bratislava (Photo 1). Procedure of the calibration of a given river section by trial error method was 10000 Iza Qf 8500 evaluated and published in [21]. For evaluation of accuracy of ] Iza Qm 1 7000 s the model simulation the following statistical indicators were 3 5500 used: coefficient of correlation R, mean error ME, mean 4000 Q [m Q absolute error MAE, mean absolute percentage error MAPE, 2500 1000 standard deviation SD, and maximum absolute error MAX. 05Aug02 10Aug02 15Aug02 20Aug02 The MAPE value of 4.8% was obtained for whole river reach between measured and simulated discharges. The mean correlation coefficient value of 0.99 for whole river reach 10000 Sturovo Qf 8500 Kienstock–Nagymaros was also calculated. These values show ] Sturovo Qm 1 7000 s a good agreement between measured and simulated discharges. 3 5500 Figure 3 presents verification of the August 2002 flood wave 4000 Q [m Q transformation in each gauging station of the simulated 2500 1000 Danube River reach. 05Aug02 10Aug02 15Aug02 20Aug02

10000 Nagymaros Qf 8500 ] Nagymaros Qm 1 7000 s 3 5500 4000

Q Q [m 2500 1000 05Aug02 10Aug02 15Aug02 20Aug02

Fig. 3 Verification of the model NLNDanube on flood occurred in August 2002

Photo 1 Danube River, Bratislava gauge station, 2002 and 2013 floods, (Photo Pekarova, 2.7.2013) Heavy precipitation occurred mainly in subbasins of the rivers Isar, , Traun, Enns, and Ybbs. Bavarian basin, for III. FORECASTING OF THE 2013 DANUBE FLOOD example, had 120 mm of precipitation in average, the basins of Inn and Salzach had 150 mm of precipitation and the basins of A. Hydrological and meteorological situation Traun, Enns and Ybbs had 150 mm of precipitation, per four Extreme flooding in Central Europe began after several days days (May 29–June 2, 2013). The subbasin of the Danube of heavy rain in late May and early June 2013. This extreme River between Ybbs and River had 60 mm of hydrological situation started on May 29, 2013 based on heavy precipitation [22]. Blöschl et al. [23] described the June 2013 rains in the upper part of the Danube basin [13]. From the flood in the Upper Danube basin, and compared it with the climatologic point of view, May 2013 was one of the three 2002, 1954, and 1899 floods. They described local wettest months of May in the past 150years in this part of the atmospheric and meteorological conditions and runoff Danube basin. generation of the 2013 flood.

ISBN: 978-1-61804-239-2 395 Advances in Environmental Sciences, Development and Chemistry

Because the heavy precipitation did not hit only the German 11500 Devin/Bratisl. Qf 10000 part of the Danube basin, but also the Austrian part of the Devín/Bratisl. Qm

] 8500

Danube basin, water levels started to increase almost -1

s 7000 3 simultaneously. The Bavarian Danube and the Inn join 5500 at . For example, water level in gauging station Passau [m Q 4000 2500 Ilzstadt started to increase already on 30 May from the level of 1000 553 cm to the level of 1102 cm. After the confluence of the 26May13 31May13 05Jun13 10Jun13 15Jun13 Bavarian Danube and Inn the wave traveled downstream the Austrian Danube changing shape and shifting the timing. 11500 Medveďov Qf 10000 Recorded flows from Austrian part of Danube were high, Medveďov Qm

] 8500 caused by heavy precipitation in basins of the rivers Traun, -1

s 7000 3 Enns and Ybbs. The culminations at the Austrian gauging 5500 stations Ybbs and Kienstock started at the evening [m Q 4000 2500 on Thursday June 4, 2013. 1000 The Slovak part of the Danube River started to increase on 26May13 31May13 05Jun13 10Jun13 15Jun13 Friday May 31, 2013. Water level increased by 280 cm per 11500 24 hours at Devin gauge. Due to temporary interruption of the Iza Qf 10000 rainfall; water level also did not raise temporary. Water levels Iza Qm ] 8500 -1 again started to increase from Sunday June 2, 2013 to s 7000 3 Thursday June 6, 2013. The culmination of the Danube River 5500

Q [m Q 4000 occurred on Thursday June 6, 2013 (at 3:15 p.m.) at Devin 2500 3 1 gauge station (974 cm, 10 640 m s ) and about two hours later 1000 at Bratislava (1034 cm, 10 641 m3s1) [22]. 26May13 31May13 05Jun13 10Jun13 15Jun13

B. Forecasting of the 2013 Danube flood 11500 Sturovo Qf 10000 We continuously forecasted the Danube discharge during Sturovo Qm 8500 ] the May/June 2013 flood on the Danube River. We used -1

s 7000 3 nonlinear river model NLNDanube, calibrated after the last 5500 large Danube flood event in August 2002. The hourly [m Q 4000 discharges from upstream water gauging station Kienstock 2500 were used as input data to the model. 1000 26May13 31May13 05Jun13 10Jun13 15Jun13 As mentioned above, water gauging station Kienstock gives a fair forecast leadtime for Bratislava. For example, travel Fig. 4 Forecasting of the June 2013 flood wave transformation by the time of the peaks of the floods from Kienstock to Bratislava in model NLNDanube, river reach Kienstock–Sturovo, stations: 3 1 September 1899 (10 870 m s at Bratislava [24], and in Devin/Bratislava, Medvedov, Iza, Sturovo. Qm – measured August 2002 (10 390 m3s1at Bratislava) was estimated with discharge, Qf– forecasted discharge values of 45 and 47 hours. Transformation of forecasted flood wave discharge at the flood occurred in August 2002. Moreover, values of the current river conditions is illustrated in Figure 4. For each part forecasted peak discharges were higher than recorded in of the Danube River reach the basic statistical characteristics stations Medvedov, Iza and Sturovo. This positive result may of forecasted and measured discharges and errors of the be caused by the proper manipulation on the water power plant forecast for June 2013 flood were calculated. Mean absolute Gabcikovo or by changes in the river regime conditions of the percentage error (MAPE) of the model between forecasted and Danube since 2002 in selected section. measured discharges reached the value of 7.6 %. The correlation coefficient between forecasted and measured 2100 1954 Ybbs 2050 1965 discharges was calculated with value of 0.978. 1991 Kienstock 2000 1997 The flood wave culmination was forecasted by model NLN 2002 March Stein Krems 1950 1899 Danube to occur later than it was recorded in reality, at water 2002August Wien 1900 2013 gauge stations Medvedov and Iza. These differences of the [km] S 1850 Bratislava culmination time occurrence in year 2013 are not significant. 1800 Komárno Therefore we can assume, that there are no significant changes 1750 Štúrovo in travel time of the floods with a peak discharge above of 1700

3 1 0 10 20 30 40 50 60 70 80 90

10 000 m s . Travel time of selected historical flood peaks is 100 110 120 130 140 t [h] illustrated in Figure 5. From Figure 5 it follows, that travel time of the June 2013 Fig. 5 Travel times of the peak discharges of the selected floods flood in river reach Ybbs–Sturovo was similar to travel time of on the Danube River reach Ybbs–Sturovo

ISBN: 978-1-61804-239-2 396 Advances in Environmental Sciences, Development and Chemistry

C. Recalibration of the model NLN-Danube 2013 Danube flood 14000 Due to results mentioned above, we recalibrated the model Kienstock 12000 Devin NLNDanube parameters for present river regime conditions Medvedov on the Danube River. Some basic characteristics and obtained 10000 Iza Sturovo ) -1 model errors are presented in Table 1 for two stations of the s 8000 3 Danube River reach. Mean absolute percentage error (MAPE) 6000 Q (m Q was calculated and it reached value of 4.75%. Mean 4000 coefficient of correlation reached the value of 0.992 for the 2000 whole Kienstock–Sturovo reach of the Danube River. 0 29May 03Jun 08Jun 13Jun a)

Table 1 Basic statistical characteristic of simulated and measured Kienstock catastrophic scenario 14000 Devin discharges and errors of the forecasting – recalibration of the model Medvedov NLNDanube parameters including the Danube flood in June 2013 12000 Iza Devin/Bratislava Sturovo 10000 Sturovo )

forecast / measured forecast / measured -1

s 8000 Mean [m3s1] 5388 / 5601 5636 / 5758 3 3 1 6000 Min [m s ] 1860 / 1968 2290 / 2231 (mQ Max [m3s1] 10 612 / 10 640 9601 / 9488 4000 Volume [mil m3] 10 261 / 10 667 10 779 / 10 965 2000 R [] 0.991 0.993 0 29May 03Jun 08Jun 13Jun ME* 213.7 121.2 b) MAPE* 6.4 4.6 Fig. 6 Transformation of the flood wave: a) simulation of the June 2013 flood between Kienstock and *ME – mean error, MAPE – mean absolute percentage error. Sturovo for present river conditions on the Danube River b) simulation of the catastrophic flood wave between Kienstock

and Sturovo for today river conditions on the Danube River

IV. SIMULATION OF CATASTROPHIC FLOOD SCENARIO FOR

PRESENT DANUBE RIVER REGIME CONDITIONS Simulated peak discharges for individual water gauging Finally, we used the recalibrated model NLNDanube for stations are presented in Table 2. simulation of transformation of the scenario catastrophic flood Results of this simulation show, that the travel time of the events. peak catastrophic flood wave could reach value of 50 hours The peak discharge of the simulated catastrophic flood wave from Kienstock to Devin, with peak discharge on value of 3 1 of the value of 14 000 m s was selected. Based on archive 12 790 m3s1 at Devin (it corresponds to water level value of records concerning historical floods on the Upper Danube, 1 120 cm, for present river conditions of the Danube River). such catastrophic flood occurred on the Danube River in Table 2 also presents simulated values of peak discharges of August 1501 [7]. The peak discharge during 1501 flood was the catastrophic flood wave scenario based on the hydrograph estimated from the peak water level in . The August of Danube flood in August 2002 between Kienstock and 1501 flood has been regarded by hydrologists as the largest Sturovo given in [12]. Results of the simulation showed the flood for the last 6001000 years on the Upper Danube. The peak travel time value of about 54 hours from Kienstock to water level of 1070 cm was reached during this flood at Stein Devin with peak discharge value of 12 430 m3s1 (it Krems [25], [26]. Moreover, experiences from the regime of corresponds to water level value of 1 072 cm, for the Danube summer floods on the Danube River show that flood in August River conditions in 2002). 1501 might been caused by heavy precipitation on the whole Bavarian and Austrian part of the Danube River. The Alpine tributaries are main sources of the water in this area of the Table 2 Values of peak discharges according to simulation of the Danube River. Their culminations gradually met and catastrophic flood wave scenario on the Danube River by model contributed to flood wave on the Danube River. NLNDanube The June 2013 flood was the largest flood recorded and Scenario peak Devin/ Iza Sturovo (simulation) Bratislava directly measured on the Danube River at Bratislava since 3 -1 Peak2013 [m s ] 10 640 9 497 9 488 1899 and it had similar meteorological conditions. Therefore, 3 1 simulation of the catastrophic scenario was based on the Peak2002 [m s ] 9 240 9 103 8 960 3 -1 hydrograph of the June 2013 flood. Wave transformation of Peak [m s ]* 12 745 11 259 11 233 3 1 the potentially catastrophic flood between Kienstock and Peak [m s ]** 12 430 10 930 10 630 Sturovo is illustrated in Figure 6 for present river regime * Scenario based on the hydrograph of June 2013 Danube flood. ** Scenario based on the hydrograph of August 2002 Danube flood. conditions of the Danube River.

ISBN: 978-1-61804-239-2 397 Advances in Environmental Sciences, Development and Chemistry

V. CONCLUSIONS [11] J. Szolgay, “Multilinear discrete cascade model for river flow routing and real time forecasting in river reaches with variable speed”, During the June 2013 flood we used nonlinear NLN Danube Proceedings of the ESF LESC Exploratory Workshop held at Bologna, river model for forecasting of the flood at Bratislava city for . p. 14, 2003. 48 hours ahead. After 2013 flood we recalibrated the model, [12] V. Mitkova, P. Pekarova, P. Miklanek, and J. Pekar, “Analysis of flood propagation changes in the Kienstock – Bratislava reach of the Danube and we used it for catastrophic flood wave simulation for River”, Hydrol. Sci. J., vol, 50, no.4, pp. 655–668, 2005. present river conditions on the Danube River. Sufficient [13] L. Blaskovicova, Z. Danacova, L. Lovasova, V. Simor, P. Skoda, accuracy of the model was verified by several statistical “Evolution of selected hydrological characteristics of the Danube in Bratislava”, Slovak Hydrometeorological Institute, SHMI Bratislava, p. criteria. Model NLNDanube for its relative simplicity and 15, 2013 (in Slovak). minimal input data requirements is very useful tool. [14] A. Svoboda, “Nonlinear cascade hydrological model NONLIN“, HOMS From our simulations it follows, that the travel time of peaks Reference Centre, WMO, Geneva. Component No. J15.2.02, 1993. [15] P. Pekarova, J. Pekar, and P. Miklanek, “River model of nonlinear of the both last big Danube floods (August 2002 and June cascade NLNDanube of Danube river between Ybbs and Nagymaros in 2013) was very similar along the Slovak part of the Danube. EXCEL 97”, Acta Hydrologica Slovaca, vol. 2, no. 2, IH SAS On the other hand, the peak water levels are continuously Bratislava, pp. 241–246, 2001 (in Slovak). rising. Today the peak water levels are higher at the same [16] E. M. Laurenson, “A catchment storage model for runoff routing”. J. Hydrol., 2, pp. 141163, 1964. discharges. While during 1899 flood the water level at [17] T.A. Malone, and I. Cordery, “An assessment of network models in Bratislava gauging station reached 970 cm at discharge of the flood forecasting”, New directions of surface water modelling, 10870 m3s1, in 2013 the water level reached 1034 cm at Proceedings of Baltimore Symposium, ed. Kavvas, M.L., IAHS publ. no 3 1 181, pp. 115–124, 1989. discharge of the 10640 m s . From the measured water levels [18] C. Corbus, “Contributions to approaching the floods propagation with and model simulations it follows, that due to man activities the help of the theory of the systems”, In: CD Proc. XXIst Conf. of the along the Austrian and Slovak part of the Danube Danube Countries on Hydrological Forecasting (Bucharest, Romania), National Institute of Meteorology and Hydrology, Bucharest, ISBN 973 (construction of dykes) as well as natural raise of sediments 0027595, 2002. and gravel accumulations in the river channel the flood [19] N.H. Crawford, R.K. Linsley, “A conceptual model of hydrologic transformation effect continually decreases and the flood cycle”, In Commission on Subterranean Waters. IAHS Publication No. thread rises. 63. IAHS Press: Wallingford; pp. 573–587, 1964. [20] V. Mitkova, R. Kubes, J. Szolgay, and P. Pekarova, “Simulation of 1899 and 1954 Danube flood waves transformation in the river reach REFERENCES Kienstock – Bratislava in the present hydraulic conditions”, Acta Hydrologica Slovaca, vol. 5, no. 1, IH SAS Bratislava, pp. 52–62, 2004 [1] P. J. Smith, L. Panziera, and K. J. Beven. "Forecasting flash floods (in Slovak). using databased mechanistic models and NORA radar rainfall [21] L. Blaskovicova, O. Horvat, K. Hlavcova, S. Kohnova, J. Szolgay J. forecasts". Hydrol. Sci. J., pp. 115, Publ. online: Jan 2014, aheadof “Methodology for postevent analysis of flash floods Svacenický Creek print 2014. case study”. In Contributions to Geophysics and Geodesy. ISSN 1335 [2] T. R. Kjeldsen, J. D. Miller, and J. C. Packman, “Modelling design 2806, vol. 41, no. 3, p. 235250, 2011. flood hydrographs in catchments with mixed urban and rural land [22] SHMI, “June 2013 Danube flood”, Slovak Hydrometeorological cover”, Hydrology Research, vol 44, no 6, pp 1040–1057, 2013. Institute, SHMI report, Bratislava, p. 39, 2013. [3] S. Y. Wu, “Potential impact of climate change on flooding in the Upper [23] G. Blöschl, T. Nester, J. Komma, J. Parajka, and R. A. P. Perdigão, Great Miami River Watershed, Ohio, USA a simulationbased “The June 2013 flood in the Upper Danube basin, and comparisons with approach”, Hydrol. Sci. J., vol. 55, no. 8, pp. 1251–1263, 2010. the 2002, 1954 and 1899 floods”, Hydrol. Earth Syst. Sci. vol. 17, no. 7, [4] S. Rozalis, E. Morin, Y. Yar, and C. Price, “Flash flood prediction using pp. 9533–9573, 2013. an uncalibrated hydrological model and radar rainfall data in a [24] H. Angelini, “Danube flood in July 1954”, Bratislava. Hydrological Mediterranean watershed under changing hydrological conditions”, Study. HMI Department of Hydrology, Bratislava, Slovak Republic, J. Hydrol., vol. 394, pp. 245–255, 2010. 1955 (in Slovak). [5] D. A. Westerman, and B. R. Clark, “Simulation of the June 11, 2010, [25] W. Kresser, Die Hochwaesser der Donau. Springer Verlag, Wien, Flood Along the Little Missouri River near Langley, Arkansas, Using a , 1957 (in German). Hydrologic Model Coupled to a Hydraulic Model”, U.S. Geological [26] A. Svoboda, P. Pekarova, and P. Miklanek, Flood hydrology of Danube Survey Scientific Investigations Report no. 5056, p. 35, 2013. between Devin and Nagymaros, publ. SVH – IH SAS, p. 96, 2000. [6] T.Y. Gan, F. Zunic, and T. Strobl, “Flood mapping of Danube River at Romania using single and multidate ERS2SAR images”, International Journal of Applied Earth observation an geoinformation, vol. 18, p. 69 81, 2012. [7] P. Pekarova, D. Halmova, V. Bacova Mitkova, P. Miklanek, J. Pekar, and P. Skoda, “Historic flood marks and flood frequency analysis of the Danube River at Bratislava, Slovakia”, J. Hydrol. Hydromech., vol. 61, no. 4, pp. 326–333, 2013 [8] P. Stanzel, B. Kahl, U. Haberl, M. Hernnegger, and H. P. Nachtnebel, “Continuous hydrological modeling in the context of real time flood forecasting in Alpine Danube tributary catchments”, XXIVth Conference of the Danubian Countries. IOP Conf. Ser.: Earth Environ. Sci., vol. 4, 012005, pp. 1–7, 2008. [9] R. Dankers, O. B. Christensen, L.Feyen, M. Kalas, and A. De Roo, “Evaluation of very highresolution climate model data for simulating flood hazards in the Upper Danube basin”, J. Hydrol., vol. 347, no. 34, pp. 319–331, 2007. [10] O. Böhm, and K. F. Wetzel, “Flood history of the Danube tributaries Lech and Isar in the of ”, Hydrol. Sci. J., vol. 51, pp. 784–798, 2006.

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