Historic flood events in NE Romania (post-1990)

References

• Adjim, H., Djedid, A., & Hamma, W. (2017). Urbanism, climate change and floods: Case of Tlemcen city in Algeria. Urbanism. Architecture. Constructions, 9(1), 71–80.
   Google Scholar
• Astite, S. W., Medjerab, A., Belabid, N. E., El Mahmouhi, N., El Wartiti, M., & Kemmon, S. (2015). Cartography of flood hazard by overflowing rivers using hydraulic modeling and geographic information system: Oued El Harrach case, (North of Algeria). Revista de Teledetección, 44, 67–79. doi: 10.4995/raet.2015.3985
   Web of Science ®Google Scholar
• Balica, S., Dinh, Q., Popescu, I., Vo, T. Q., & Pham, D. Q. (2014). Flood impact in the Mekong Delta, Vietnam. Journal of Maps, 10(12), 257–268. doi: 10.1080/17445647.2013.859636
   Web of Science ®Google Scholar
• Banskota, A., Kayastha, N., Falkowski, M. J., Wulder, M. A., Froese, R. E., & White, J. C. (2014). Forest monitoring using Landsat time series data: A review. Canadian Journal of Remote Sensing, 40(5), 362–384. doi: 10.1080/07038992.2014.987376
   Web of Science ®Google Scholar
• Bhatt, C. M., Rao, G. S., Farooq, M., Manjusree, P., Shukla, A., Sharma, S. V., … Dadhwal, V. K. (2016). Satellite-based assessment of the catastrophic Jhelum floods of September 2014, Jammu & Kashmir, India. Geomatics, Natural Hazards and Risk, 7, 1–19. doi: 10.1080/19475705.2014.949877
   Web of Science ®Google Scholar
• Bonansea, M., Rodriguez, M. C., Pinotti, L., & Ferrero, S. (2015). Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina). Remote Sensing of Environment, 158, 28–41. doi: 10.1016/j.rse.2014.10.032
   Web of Science ®Google Scholar
• Brinke, W. B. M., Knoop, J., Muilwijk, H., & Ligtvoet, W. (2017). Social disruption by flooding, a European perspective. International Journal of Disaster Risk Reduction, 21, 312–322. doi: 10.1016/j.ijdrr.2017.01.011
   Web of Science ®Google Scholar
• Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors. Remote Sensing of Environment, 113, 893–903. doi: 10.1016/j.rse.2009.01.007
   Web of Science ®Google Scholar
• Chen, X., Vierling, L., & Deering, D. (2005). A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment, 98, 63–79. doi: 10.1016/j.rse.2005.05.021
   Web of Science ®Google Scholar
• Cojoc, G., Romanescu, G., & Tirnovan, A. (2015). Exceptional floods on a developed river. Case study for the Bistrita River from the Eastern Carpathians (Romania). Natural Hazards, 77(3), 1421–1451. doi: 10.1007/s11069-014-1439-2
   Web of Science ®Google Scholar
• Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Review ArticleDigital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9), 1565–1596. doi: 10.1080/0143116031000101675
   Web of Science ®Google Scholar
• Cutaia, L., Massacci, P., & Roselli, I. (2004). Analysis of Landsat 5 TM images for monitoring the state of restoration of abandoned quarries. International Journal of Surface Mining, Reclamation and Environment, 18(2), 122–134. doi: 10.1080/13895260412331295385
   Google Scholar
• Ding, H., & Elmore, A. J. (2015). Spatio-temporal patterns in water surface temperature from Landsat time series data in the Chesapeake Bay, U.S.A. Remote Sensing of Environment, 168, 335–348. doi: 10.1016/j.rse.2015.07.009
   Web of Science ®Google Scholar
• Du, J., Feng, X., Wang, Z., Huang, Y. S., & Ramadan, E. (2002). The methods of extracting water information from spot image. Chinese Geographical Science, 12(1), 68–72. doi: 10.1007/s11769-002-0073-1
   Web of Science ®Google Scholar
• Du, Y., Teillet, P. M., & Cihlar, J. (2002). Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection. Remote Sensing of Environment, 82, 123–134. doi: 10.1016/S0034-4257(02)00029-9
   Web of Science ®Google Scholar
• Faccini, F., Luino, F., Sacchini, A., Turconi, L., & De Graff, J. (2015). Geohydrological hazards and urban development in the Mediterranean area: An example from genoa (Liguria, Italy). Natural Hazard and Earth System Science, 15, 2631–2652. doi: 10.5194/nhess-15-2631-2015
   Web of Science ®Google Scholar
• Gitas, I. Z., Polychronaki, A., Katagis, T., & Mallinis, G. (2008). Contribution of remote sensing to disaster management activities: A case study of the large fires in the Peloponnese, Greece. International Journal of Remote Sensing, 29(6), 1847–1853. doi: 10.1080/01431160701874553
   Web of Science ®Google Scholar
• Goetz, S. J., Gardiner, N., & Viers, J. H. (2008). Monitoring freshwater, estuarine and nearshore benthic ecosystems with multi-sensor remote sensing: An introduction to the special issue. Remote Sensing of Environment, 112, 3993–3995. doi: 10.1016/j.rse.2008.05.016
   Web of Science ®Google Scholar
• Hapciuc, O. E., Romanescu, G., Minea, I., Iosub, M., Enea, A., & Sandu, I. (2016). Flood susceptibility analysis of the cultural heritage in the Sucevita catchment (Romania). International Journal of Conservation Science, 7(2), 501–510.
   Web of Science ®Google Scholar
• Ireland, G., Volpi, M., & Petropoulos, G. P. (2015). Examining the capability of supervised machine learning classifiers in extracting flooded areas from landsat TM imagery: A case study from a Mediterranean flood. Remote Sensing, 7, 3372–3399. doi: 10.3390/rs70303372
   Web of Science ®Google Scholar
• Irish, R. R. (1998). Landsat 7 science data users handbook. Greenbelt, MD: Landsat Project Science Office-NASA’s Goddard Space Flight Center.
   Google Scholar
• Jiang, H., Feng, M., Zhu, Y., Lu, N., Huang, J., & Xiao, T. (2014). An automated method for extracting rivers and lakes from landsat imagery. Remote Sensing, 6, 5067–5089. doi: 10.3390/rs6065067
   Web of Science ®Google Scholar
• Kaufman, Y. J., Wald, A. E., Remer, L. A., Bo-Cai Gao, L. A., Rong-Rong Li, L. A., & Flynn, L. (1997). The MODIS 2.1 μm channel-correlation with visible reflectance for use in remote sensing of Aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35(5), 1286–1298. doi: 10.1109/36.628795
   Web of Science ®Google Scholar
• Kaufman, Y. J. (1998). Atmospheric effect on spectral signature-measurements and corrections. IEEE Transactions on Geoscience and Remote Sensing, 26, 441–450. doi: 10.1109/36.3048
   Web of Science ®Google Scholar
• Kundzewicz, Z. W., Pińskwar, I., & Brakenridge, G. R. (2013). Large floods in Europe, 1985–2009. Hydrological Sciences Journal, 58(1), 1–7. doi: 10.1080/02626667.2012.745082
   Web of Science ®Google Scholar
• Li, W., Du, Z., Ling, F., Zhou, D., Wang, H., Gui, Y., … Zhang, X. (2013). A comparison of land surface water mapping using the Normalized Difference Water Index from TM ETM+ and ALI. Remote Sensing, 5, 5530–5549. doi: 10.3390/rs5115530
   Web of Science ®Google Scholar
• Lillesand, T. M., & Kiefer, R. W. (1994). Remote sensing and image interpretation. Toronto: John Wiley and Sons.
   Google Scholar
• Lobo, F. L., Costa, M. P. F., & Novo, E. M. L. M. (2015). Time-series analysis of Landsat-MSS/TM/OLI images over Amazonian waters impacted by gold mining activities. Remote Sensing of Environment, 157, 170–184. doi: 10.1016/j.rse.2014.04.030
   Web of Science ®Google Scholar
• Magliulo, P., Bozzi, F., & Pignone, M. (2016). Assessing the planform changes of the Tammaro River (southern Italy) from 1870 to 1955 using a GIS-aided historical map analysis. Environmental Earth Sciences, 75(4), 257. doi: 10.1007/s12665-016-5266-5
   Web of Science ®Google Scholar
• Magliulo, P., & Cusano, A. (2016). Geomorphology of the Lower Calore River alluvial plain (Southern Italy). Journal of Maps, 12(5), 1119–1127. doi: 10.1080/17445647.2015.1132277
   Web of Science ®Google Scholar
• Mayer, R., Plank, C., Bohner, A., Kollarits, S., Corsini, A., Ronchetti, F., … Jindra, P. (2008). Monitor: Hazard monitoring for risk assessment and risk communication. Georisk, 2(4), 193–222. doi: 10.1080/17499510802506139
   Google Scholar
• McFeeters, S. K. (1996). The use of normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17, 1425–1432. doi: 10.1080/01431169608948714
   Web of Science ®Google Scholar
• Miklín, J., & Hradecký, J. (2016). Confluence of the Morava and Dyje Rivers: A century of landscape changes in maps. Journal of Maps, 12(4), 630–638. doi: 10.1080/17445647.2015.1068714
   Web of Science ®Google Scholar
• Morris, N. (2008). Low-cost remote sensing and GIS for regional disaster risk reduction, north west Costa Rica. Journal of Maps, 4, 23–38. doi: 10.1080/jom.2008.9711032
   Google Scholar
• Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmitha, J., … Ip, A. (2016). Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. Remote Sensing of Environment, 174, 341–352. doi: 10.1016/j.rse.2015.11.003
   Web of Science ®Google Scholar
• NASA. (2011). Landsat 7 science data users handbook Landsat project science office at NASA’s Goddard Space Flight Center in Greenbelt, 186. Retrieved from http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf
   Google Scholar
• Olariu, P., Obreja, F., & Obreja, I. (2009). Unele aspecte privind tranzitul de aluviuni din bazinul hidrografic Trotus si de pe sectorul inferior al raului Siret in timpul viiturilor exceptionale din anii 1991 si 2005 (In romanian). Analele Universitatii Stefan cel Mare Suceava, Geografie, XVIII, 93–104.
   Google Scholar
• Otukei, J., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27–S31. doi: 10.1016/j.jag.2009.11.002
   Web of Science ®Google Scholar
• Ouma, Y. O., & Tateishi, R. (2014). Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: Methodological overview and case study assessment. Water, 6, 1515–1545. doi: 10.3390/w6061515
   Web of Science ®Google Scholar
• Petrisor, A. I., Petre, R., & Meita, V. (2016). Difficulties in achieving social sustainability in a Biosphere Reserve. International Journal of Conservation Science, 7(1), 123–136.
   Web of Science ®Google Scholar
• Radevski, I., & Gorin, S. (2017). Floodplain analysis for different return periods of river Vardar in Tikvesh Valley (Republic of Macedonia). Carpathian Journal of Earth and Environmental Sciences, 12(1), 179–187.
   Web of Science ®Google Scholar
• Romanescu, G., Hapciuc, O. E., Minea, I., & Iosub, M. (2017). Flood vulnerability assessment in the mountain-plateau transition zone. Case study for Marginea village (Romania). Journal of Flood Risk Management, doi: 10.1111/jfr3.12249
   PubMed Web of Science ®Google Scholar
• Romanescu, G., & Nistor, I. (2011). The effect of the July 2005 catastrophic inundations in the Siret River’s Lower Watershed, Romania. Natural Hazards, 57(2), 345–368. doi: 10.1007/s11069-010-9617-3
   Web of Science ®Google Scholar
• Romanescu, G., & Stoleriu, C. C. (2013a). An inter-basin backwater overflow (the Buhai Brook and the Ezer reservoir on the Jijia River, Romania). Hydrological Processes, 28(7), 3118–3131. doi: 10.1002/hyp.9851
   Web of Science ®Google Scholar
• Romanescu, G., & Stoleriu, C. C. (2013b). Causes and effects of the catastrophic flooding on the Siret River (Romania) in July-August 2008. Natural Hazards, 69, 1351–1367. doi: 10.1007/s11069-012-0525-6
   Web of Science ®Google Scholar
• Romanescu, G., & Stoleriu, C. C. (2017). Exceptional floods in the Prut basin, Romania, in the context of heavy rains in the summer of 2010. Natural Hazards and Earth System Sciences, 17, 381–396. doi: 10.5194/nhess-17-381-2017
   Web of Science ®Google Scholar
• Romanescu, G., Stoleriu, C. C., & Romanescu, A. M. (2011). Water reservoirs and the risk of accidental flood occurrence. Case study: Stanca–Costeşti reservoir and the historical floods of the Prut river in the period July–August 2008, Romania. Hydrological Processes, 25(13), 2056–2070. doi: 10.1002/hyp.7957
   Web of Science ®Google Scholar
• Romanescu, G., Zaharia, C., Sandu, A. V., & Juravle, D. T. (2015). The annual and multi-annual variation of the minimum discharge in the Miletin catchment (Romania). An important issue of water conservation. International Journal of Conservation Science, 6(4), 729–746.
   Web of Science ®Google Scholar
• Roy, D. P., Kovalskyy, V., Zhanga, H. K., Vermote, E. F., Yana, L., Kumar, S. S., & Egorova, A. (2016). Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 185, 57–70. doi: 10.1016/j.rse.2015.12.024
   PubMed Web of Science ®Google Scholar
• Sakai, T., Hatta, S., Okumura, M., Hiyama, T., Yamaguchi, Y., & Inoue, G. (2015). Use of Landsat TM/ETM+ to monitor the spatial and temporal extent of spring breakup floods in the Lena River, Siberia. International Journal of Remote Sensing, 36(3), 719–733. doi: 10.1080/01431161.2014.995271
   Web of Science ®Google Scholar
• Sami, K., Mohsen, B. A., Afet, K., & Fouad, Z. (2013). Hydrological modeling using GIS for mapping flood zones and degree flood risk in zeuss-koutine basin (south of Tunisia). Journal of Environmental Protection, 4, 1409–1422. doi: 10.4236/jep.2013.412161
   Google Scholar
• Sandholt, I., Nyborg, L., Fog, B., Lô, M., Bocoum, O., & Rasmussen, K. (2003). Remote sensing techniques for flood monitoring in the Senegal River Valley. Geografisk Tidsskrift - Danish Journal of Geography, 103, 71–81. doi: 10.1080/00167223.2003.10649481
   Google Scholar
• Sanyal, J., & Lu, X. X. (2004). Application of remote sensing in flood management with special reference to monsoon Asia: A review. Natural Hazards, 33(2), 283–301. doi: 10.1023/B:NHAZ.0000037035.65105.95
   Web of Science ®Google Scholar
• Schott, J. (1997). Remote sensing – the image chain approach. New York, NY: Oxford University Press.
   Google Scholar
• Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sensing of Environment, 75, 230–244. doi: 10.1016/S0034-4257(00)00169-3
   Web of Science ®Google Scholar
• Tebbs, E. J., Remedios, J. J., & Harper, D. M. (2013). Remote sensing of chlorophyll-a as a measure of cyanobacterial biomass in Lake Bogoria, a hypertrophic, saline–alkaline, flamingo lake, using Landsat ETM+. Remote Sensing of Environment, 135, 92–106. doi: 10.1016/j.rse.2013.03.024
   Web of Science ®Google Scholar
• Teeuw, R. M., Mcwillian, N., Whiteside, M., & Zukowskyj, P. M. (2005). Geographical information sciences and fieldwork. London: Royal Geographical Society.
   Google Scholar
• Thomas, R. F., Kingsford, R. T., Lu, Y., Cox, S. J., Sims, N. C., & Hunter, S. J. (2015). Mapping inundation in the heterogeneous floodplain wetlands of the Macquarie Marshes, using Landsat Thematic Mapper. Journal of Hydrology, 524, 194–213. doi: 10.1016/j.jhydrol.2015.02.029
   Web of Science ®Google Scholar
• Tulbure, M. G., Broicha, M., Stehman, S. V., & Kommareddy, A. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment, 178, 142–157. doi: 10.1016/j.rse.2016.02.034
   Web of Science ®Google Scholar
• Van Alphen, J., Martini, F., Loat, R., Slomp, R., & Passchier, R. (2009). Flood risk mapping in Europe, experiences and best practices. Journal of Flood Risk Management, 2(4), 285–292. doi: 10.1111/j.1753-318X.2009.01045.x
   Web of Science ®Google Scholar
• Visser, F. (2014). Rapid mapping of urban development from historic Ordonance survey maps: An application for fluvial flood risk in Worcester. Journal of Maps, 10(3), 276–288. doi: 10.1080/17445647.2014.893847
   Web of Science ®Google Scholar
• Wang, Y., Colby, J. D., & Mulcahy, K. A. (2002). An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data. International Journal of Remote Sensing, 23(18), 3681–3696. doi: 10.1080/01431160110114484
   Web of Science ®Google Scholar
• Wang, Y. (2004). Using Landsat 7 TM data acquired days after a flood event to delineate the maximum flood extent on a coastal floodplain. International Journal of Remote Sensing, 25(5), 959–974. doi: 10.1080/0143116031000150022
   Web of Science ®Google Scholar
• Xu, H. (2006). Modification of Normalized Difference Water Index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. doi: 10.1080/01431160600589179
   Web of Science ®Google Scholar
• Yang, Y., Liu, Y., Zhou, M., Zhang, S., Zhan, W., Sun, C., & Duan, Y. (2015). Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach. Remote Sensing of Environment, 171, 14–32. doi: 10.1016/j.rse.2015.10.005
   Web of Science ®Google Scholar
• Zhang, F., Tiyip, T., Kung, H., Johnson, V. C., Wang, J., & Nurmemet, I. (2016). Improved water extraction using Landsat TM/ETM+ images in Ebinur Lake, Xinjiang, China. Remote Sensing Applications: Society and Environment, 4, 109–118. doi: 10.1016/j.rsase.2016.08.001
   Google Scholar
• Zhu, Z., Wang, S., & Woodcock, C. E. (2015). Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsat 4–7, 8, and Sentinel 2 images. Remote Sensing of Environment, 159, 269–277. http://gis2.rowater.ro doi: 10.1016/j.rse.2014.12.014
   Web of Science ®Google Scholar