Flood Modeling and Vulnerability Assessment in Ialomita River Basin,

Maria Cheveresan Technical University of Civil Engineering , Romania [email protected]

Abstract

The floods from recent years are becoming more and more severe causing death of people and material losses. The floods with the probability of excedance of 1% are more frequent, phenomena due mainly to the climate change. In this context the lack of hazard and vulnerability maps at a river basin scale which should prevent the construction of new buildings in flood prone areas, lead to inevitable losses on long term. This paper treats the high water flow regime in Ialomita river basin and simple and efficient methods for assessing the vulnerability. Among the causes which generate floods are heavy rainfall and dam breaks which can produce real disasters downstream the section where they occur. Mathematical modeling, with the latest software generation, gives complete and detailed answers regarding flood extensions lag time for flood propagation, maximum velocities and depths in the studied area. Two accidental flood propagation models were created in case of four scenarios of dam failure, using Mike 11 and Mike 21. Comparing the results of these models differences were found due mainly to input data, noting that the 2D model gives the most accurate results. The resources invested in the creation of 2D models overpasses, as it was expected the creation of a 1D model from the data acquisition point of view but also because of the large computation time (aprox.30 hours for 190 de km river stretch). The vulnerability on Dridu-Tandarei reach in the worst case scenario of Dridu dam failure was evaluated based on economical and social indices as well as on the characteristics of the flood waves. Taking into account the lack of data and difficulties which may appear when assessing the vulnerability by tens of indices, a statistical approach was proposed leading to fast results.

Keywords: Flooding, hazard maps, probabilities of exceedance, vulnerability assessment, statistics

Introduction Ialomita river basin is situated in the south east part of Romania having 10.430 km2. In 2005 a severe flood caused great economical damage which practically reached the magnitude of 1% flood. Based on this event two hydraulic 1D and 2D models were created and calibrated and further used for assessing hazard maps for Ialomita river (main river in the basin) and for modeling dam break failure scenarios. The worst case scenario for dam failure was taken into account for vulnerability assesment in a statistical approach offering information for further evaluation of qualitative risk in Ialomita river basin.

BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 1

Figure 1. Location of Ialomita river basin

Propagation of the flood wave caused by dam failure scenarios Dridu dam is situated at the junction of Ialomita River with Prahova River having an important role in the flood attenuation which can occur when the two flood waves are compounded. Four failure scenarios were proposed for Dridu dam taking into account the exploitation history and the incidents which occurred since the putting into operation (Table 1 [Stematiu&Sirbu, 2009). The worst scenario (2b), for which it is considered that one of the front tile of the dam structure would slide downstream, generates a flood wave with a maximum discharge of 2361 m3/s which is close to the 0,1% maximum discharge. Further in this analysis the worst case scenario 2b will be considered. Table 1 [Stematiu&Sirbu, 2009] Scenario Maximum inflow Maximum discharge of the flood wave Duration of the flood caused discharge (m3/s) caused by the dam failure (m3/s) by dam failure (h) 1 - 1349 47 2a 1280 2237 116 2b 1330 2361 76 3 420 1398 144

BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 2 1D model setup For the 1D simulation of the worst case scenario a model was created and calibrated in Mike 11 for Ialomita river stretch between Dridu and Tandarei. The boundary conditions used in this 1D model were the accidental flood wave at the upstream end and the rating curve at the downstream end. The extension of the flood in case of 2b scenario can be seen in Fig. 1.

Figure 1. Maximum extension of the flood in case of the 2b failure scenarios obtained through 1D modeling 2D model setup For the creation of the 2D model on the area of interest between Dridu and Tandarei, HD module from Mike 21 was used setting the following elements: -DTM in the modeled area as fix grid having 40m x 50m resolution (Fig.2) -Simulation time step (10s) and simulation time (10 days) – the real time for computation on an Intell 2 core duo 4GB DIM was in average 30 hours -Boundary conditions -Drying (0.02 m)/flooding (0.03 m) depths conditions -Initial conditions grid with initial water level on the modeled area -Eddy viscosity parameter – 0.04 m2/s -Roughness coefficients as Manning M ranging between 20-40 m1/3/s

Figure 2. DTM on Dridu-Tandarei river stretch

BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 3 The simulation was done in several phases by generating Hot Start files at the end of each phase which was used as initial conditions for the next ones. Maximum depths and velocities were obtained for each grid cell in the modeled area (Fig. 3 and 4).

Figure 3. Maximum water depths on Dridu-Tandarei River stretch in case of 2b scenario

Figure 4. Maximum velocities on Dridu -Tandarei River stretch in case of 2b scenario

Comparison between 1D and 2D model on Dridu Tandarei river stretch The results from the 1D model and those for the 2D model in case of the worst scenario for Dridu dam failure were compared in order to determine advantages and disadvantages of using these models. The first comparison aspect referred to data used in the two models. 1D models require less topographical data than 2D models, where continuous information is required with a precision of cm. This difference is reflected in the results meaning that 1D models provide sparsely results in the longitudinal profile (sometimes based on interpolated cross sections) and constant in transversal direction. The 2D models

BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 4 offer much more results in a continuous manner like maximum depths and velocities grids. From the precision point of view, 1D models provide a flood area by intersection with the DTM. The 2D models calculate through the 2D Saint Venant equations much more precise results for each grid cell in the river bed and flood area. From the maximum discharge point of view the two models were analyzed in 5 significant cross sections along the modeled stretch. The discharges obtained through 2D modeling are with approximately 28% lower than the results from the 1D model. The propagation times are in average with 40% higher for the 2D model than the 1D model results.

Figure 5. 1D propagation in Dridu, Cosereni, , and Tandarei cross sections

Figure 6. 2D propagation in Dridu, Cosereni, Ciochina, Slobozia and Tandarei cross sections

Comparing the water depths in the two models it came out that the differences are in both directions, positive as well as negative (Fig.7)

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Figure 7. Differences in water depths for the 1D and 2D models Vulnerability assessment in Ialomita River Basin In order to asses the vulnerability in case of Dridu dam a list of vulnerability indicators were proposed (Table 2). Table 2 Vulnerability Indicators Indicators’ component abreviation Social Total number of inhabitants in the affected area NLZA Total number of inhabitants over 70 years of age NL70 Total number of children under 3 years of age NC3 Economical Total number of affected houses NCA Total numer of bridges NP Total length of affected roads LDA Total length of affected railways LCFA Total surface of affected agricultural terrain STAA Total surface of affected forests SPA Total number of cows in affected area NBZA Total number of pigs in affected area NPZA Total number of sheep in affected area NOZA Total number of goats in affected area NCZA Environmental Surface of protected areas SZP Total number of landfill deposits in the affected area NDDZA

A statistical approach allows for a social and economical assessment of the vulnerability with good precision and limited resources. The indicators proposed for the assessment of the vulnerability offer qualitative information about the vulnerable objects in the analyzed area. From this total only 10% will produce real damages. According to the data published on the National Institute of Statistics and the Ialomita County Institute of Statistics websites the population density was 66,6 inhabitants/km2 in 2002. Knowing the flood extension area, it is possible to assess the total surface of the flooded settlements and consequently the total number of affected inhabitants. The same source provides information about the

BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 6 population division on age categories. In case of flooding or other major disasters the age plays an important role in evacuating the population, persons over 70 of age and children under 3 years of age being much more vulnerable than the rest of population. Statistical data regarding population distribution on age categories in percentages in Ialomita County and the distribution of the number of inhabitants in every settlement in Ialomita County can be found in Tables 3 and 4: Table 3.Population on age classes in percentages for Ialomita County

GRUPA DE VARSTA

SEXUL POPULATIA REGIUNEA DE DEZVOLTARE STABILA 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75 ani JUDETUL TOTAL ani ani ani ani ani ani ani ani ani ani ani ani ani ani ani si peste A 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 IALOMITA 100.0 5.4 5.6 7.5 7.2 6.9 7.7 9.5 5.0 6.1 6.9 6.1 5.0 5.7 5.8 4.5 5.0

Table 4. Number of inhabitants in every settlement in Ialomita County

TOTAL 0 - 14 ANI 15 - 59 ANI 60 de ANI SI PESTE Ambele Ambele Ambele Ambele MUNICIPIUL / ORASUL / COMUNA sexe Masculin Feminin sexe Masculin Feminin sexe Masculin Feminin sexe Masculin Feminin

IALOMITA 296572 146361 150211 55022 28165 26857 179065 90986 88079 62485 27210 35275 MUNICIPII SI ORASE 115560 56937 58623 21356 10955 10401 80705 40180 40525 13499 5802 7697 SLOBOZIA 1) 52710 26267 26443 8932 4574 4358 39154 19694 19460 4624 1999 2625 FETESTI 1) 33294 16291 17003 6536 3340 3196 21979 10895 11084 4779 2056 2723 1) 17094 8311 8783 2975 1548 1427 11827 5773 6054 2292 990 1302 TANDAREI 12462 6068 6394 2913 1493 1420 7745 3818 3927 1804 757 1047 COMUNE 181012 89424 91588 33666 17210 16456 98360 50806 47554 48986 21408 27578 ADANCATA 3115 1479 1636 427 225 202 1463 752 711 1225 502 723 ALBESTI 2732 1367 1365 475 252 223 1390 736 654 867 379 488 2504 1233 1271 437 217 220 1425 742 683 642 274 368 AMARA 7627 3740 3887 1365 686 679 4889 2452 2437 1373 602 771 ANDRASESTI 2311 1118 1193 421 214 207 1312 663 649 578 241 337 ARMASESTI 7310 3644 3666 2431 1284 1147 3778 1910 1868 1101 450 651 2944 1478 1466 452 230 222 1426 763 663 1066 485 581 3404 1660 1744 517 257 260 1574 804 770 1313 599 714 BARCANESTI 3978 1916 2062 677 326 351 1909 974 935 1392 616 776 BORDUSANI 5340 2659 2681 1259 632 627 3122 1622 1500 959 405 554 BRAZII 3507 1711 1796 457 236 221 1713 888 825 1337 587 750 BUCU 5287 2628 2659 1056 553 503 3165 1611 1554 1066 464 602 CAZANESTI 3641 1789 1852 709 380 329 2201 1099 1102 731 310 421 CIOCARLIA 889 436 453 132 68 64 409 221 188 348 147 201 CIOCHINA 3601 1754 1847 636 313 323 1876 945 931 1089 496 593 CIULNITA 2556 1259 1297 480 251 229 1465 754 711 611 254 357 COCORA 3764 1844 1920 655 329 326 1916 998 918 1193 517 676 COSAMBESTI 3518 1775 1743 608 325 283 2079 1110 969 831 340 491 COSERENI 7031 3534 3497 1317 699 618 3863 1977 1886 1851 858 993 DRAGOESTI 1161 589 572 163 93 70 549 290 259 449 206 243 DRIDU 5171 2525 2646 796 422 374 2640 1350 1290 1735 753 982 FACAENI 5953 2966 2987 1281 644 637 3328 1735 1593 1344 587 757 FIERBINTI-TARG 5253 2588 2665 855 441 414 2865 1457 1408 1533 690 843 GARBOVI 4457 2196 2261 696 372 324 2267 1143 1124 1494 681 813 GHEORGHE DOJA 2854 1375 1479 416 210 206 1583 789 794 855 376 479 GHEORGHE LAZAR 2497 1245 1252 420 217 203 1451 779 672 626 249 377 1649 864 785 318 170 148 999 529 470 332 165 167 GRINDU 2438 1196 1242 388 194 194 1198 622 576 852 380 472 GRIVITA 7107 3573 3534 1419 708 711 3818 2047 1771 1870 818 1052 ION ROATA 3726 1828 1898 714 360 354 2006 1019 987 1006 449 557 3791 1890 1901 582 292 290 2037 1091 946 1172 507 665 4673 2251 2422 793 388 405 2800 1405 1395 1080 458 622 MIHAIL KOGALNICEANU 6248 3109 3139 1198 604 594 3480 1815 1665 1570 690 880 MILOSESTI 3091 1521 1570 559 270 289 1581 836 745 951 415 536 MOVILA 2221 1117 1104 492 257 235 1282 668 614 447 192 255 MOVILITA 4816 2381 2435 877 465 412 2474 1265 1209 1465 651 814 MUNTENI-BUZAU 3867 1895 1972 737 359 378 1993 1021 972 1137 515 622 PERIETI 3667 1829 1838 673 337 336 2118 1123 995 876 369 507 3376 1657 1719 535 270 265 1686 890 796 1155 497 658 SALCIOARA 2613 1304 1309 546 290 256 1411 718 693 656 296 360 SAVENI 5635 2752 2883 1019 502 517 3167 1636 1531 1449 614 835 SCANTEIA 4364 2184 2180 810 421 389 2389 1277 1112 1165 486 679 SFANTU GHEORGHE 2180 1049 1131 362 196 166 1101 553 548 717 300 417 SINESTI 2603 1264 1339 493 238 255 1318 674 644 792 352 440 1855 943 912 408 227 181 1149 590 559 298 126 172 SUDITI 2302 1129 1173 422 203 219 1257 661 596 623 265 358 2017 1009 1008 383 180 203 1137 611 526 497 218 279 VALEA MACRISULUI 2111 1056 1055 313 161 152 1032 536 496 766 359 407 VLADENI 2257 1115 1142 487 242 245 1269 655 614 501 218 283 From the economical point of view the indicators used for assessing the vulnerability have a general character and don’t need too detailed information about the affected houses. The materials from which the house is built, the number of floors and the age of the building have a great influence upon the degree of vulnerability to floods. Still on large scale this information is not available in Romania and its collection requires a lot of time and financial effort. A statistical approach which offers enough information for the vulnerability assessment represents in this case a considerable alternative. The communication routes are vulnerable to floods being subject of both direct (damage of the road or railway structure) and indirect (interrupted transport of people and goods) losses. The intersection of the flooded area with the transport networks can provide the total length of affected roads and railways and the total number of affected

BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 7 bridges and culverts. Through the same type of analysis the total surface of affected agricultural terrain can be determined. The vulnerability analysis is done at a macro scale and doesn’t include all different types of cultivation patterns that can modify each year. Forests are vulnerable to floods through wood alteration generating economical damages. For the environmental component protected areas and the landfill deposits were considered, first in order to be protected and the second one as generating accidental pollution. The cultural and natural resources within protected areas can be affected by floods leading to possible loss of the social identity for the nearby communities. In case of floods the landfill deposits can be considered potential pollution sources which can danger human or animals health in that area. In order to statistically asses the vulnerability at a settlement level the idea of assessment of 1 hectare of the settlement area was introduced. The assessment of the vulnerability on this unitary area was based on ortophotos and field trips for confirmation of the results. This is a GIS analysis in which one square of 100x100 m2 is delineated and randomly moved on the settlement area. Counting the number of houses contained in each position of the square an average number of houses can be established in this area of 1 ha. The movement of the square has to be done at least 30 times in order to ensure a statistical population of values on which to base the results. One condition of the statistical population is that it is homogenously distributed in the analyzed domain, thus the square can be positioned in areas with no houses (Fig. 8).

Figura 8. The inventory of the number of houses per equivalent settlement hectar –example Moldoveni settlement Once the average number of houses was established on one settlement the procedure can be repeated on other two or three different settlements making an average on the whole area obtaining an average number of houses in Ialomita river basin – 6,4 houses/hectare in this case. In order to easily assess the vulnerability of the three components (social, economical and environmental) a GIS tool was developed which allows by the intersection of the flooded area with several feature classes (roads, railways, bridges and land cover) to automatically obtain a report with the total length of railroads, roads and a total surface of vulnerable settlements. The conceptual model of the developed tool can be seen below (Fig. 9):

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Figure 9. Conceptual model of the GIS tool for the economical component of the vulnerability assessment This tool can be used outside the GIS environment through an interface where the spatial layers that will participate at the vulnerability assessment will be selected from a drop down list. The application is based on a *.mxd file which is created prior to the usage of the GIS tool, where the flooded area, communication routes, land use, bridges and protected areas feature classes are added to the map (Fig. 10).

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Figure 10. The interface of the GIS application tool for vulnerability assessment at a river basin scale

Running the GIS tool application for the flood extension in the worst scenario of Dridu dam failure the following components were automatically identified (Table 5): Table 5 Vulnerable component Number/length [km]/ area [ha] Bridges (NP) 7 County and Settlement roads (LCFA) 7,84 Railroads (LDA) 3,8 Agricultural land (STAA) 1252 Forests (SPA) 495 Settlements 135

Based on these data the other indicators can be evaluated: - NLZA can be obtained by multiplying the population density with the total area of vulnerable settlements; the results is 900 inhabitants in the affected area - NL70 and NC3 indicators can be evaluated knowing that 5,4% of the total population is under 3 years of age and 4,5% are over 70 years of age NC3 = 4,5% *NLZA; NC3= 41 (1) NP70 = 5,4% * NLZA ; NP70 = 49 (2) - NCA = total area of vulnerable settlements x 6,4; NCA = 864 (3) - Urziceni, Manasia and Cosambesti landfill deposits were identified in the analyzed area; NDDZA=3

BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 10 Conclusions

For the simulation of the flood wave propagation in case of Dridu dam failure two models were built and run in order to compare the results and decide which model is more suited for flood analysis especially when vulnerability assessment is required. Because the accidental wave propagation is done in different flow environments (cross sections in the river bed for 1D model and DTM for 2D model) the discharges and water levels differ in the two cases. In case of the 2D model the discharges are 28% lower than in case of the 1D model. In general, water depth is lower in the flood plain area in case of the 2D model. This is due to the fact that for the 1D case the flood extent and consequently the water depths are obtain through a simple interpolation of the computed water levels in the cross sections. In case of the 2D model the water levels/depths are obtained from the solving of Saint Venant equations. In the 1D model the water depths are higher than in the 2D model because of the difference between the DTM and the linear approximation of the water level between to cross sections. Comparing the two 1D and 2D models the conclusion is that 2D models offer more precise and detailed information in case of floods especially when a vulnerability analysis is required. Still because of the great resources such models require it is indicated to limit 2D modeling only in the settlement areas. The vulnerability can be assessed through several methods, having short or long lists of vulnerability. A simplified statistical approach is recommended in the countries or areas where there are not enough data for vulnerability assessment which offers a quick and efficient image of the vulnerability. This method uses statistical free data from the National Institute of Statistics. The statistical indicator for the assessment of the vulnerability of 1 ha can be applied at all scales its costs being much more reduced than in the case of field campaigns, having the advantage of the spatial positioning, an information which is not offered by the national census yet. The GIS tool automatically identifies the number of households, roads, railways, forests and agricultural terrain affected by flood using only the shapefiles with these assets and with the flood extension. It can be used outside the GIS environment

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