Natural Hazards

, Volume 79, Issue 2, pp 735–754 | Cite as

Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models

  • Salvatore Manfreda
  • Caterina Samela
  • Andrea Gioia
  • Giuseppe Gerardo Consoli
  • Vito Iacobellis
  • Luciana Giuzio
  • Andrea Cantisani
  • Aurelia Sole
Original Paper

Abstract

The identification of flood-prone areas is a critical issue becoming everyday more pressing for our society. A preliminary delineation can be carried out by DEM-based procedures that rely on basin geomorphologic features. In the present paper, we investigated the dominant topographic controls for the flood exposure using techniques of pattern classification through linear binary classifiers based on DEM-derived morphologic features. Our findings may help the definition of new strategies for the delineation of flood-prone areas with DEM-based procedures. With this aim, local features—which are generally used to describe the hydrological characteristics of a basin—and composite morphological indices are taken into account in order to identify the most significant one. Analyses are carried out on two different datasets: one based on flood simulations obtained with a 1D hydraulic model, and the second one obtained with a 2D hydraulic model. The analyses highlight the potential of each morphological descriptor for the identification of the extent of flood-prone areas and, in particular, the ability of one geomorphologic index to represent flood-inundated areas at different scales of application.

Keywords

Flood hazard DEM Terrain analysis Geomorphic approaches Ungauged basins 

References

  1. Bridgham SD, Cadillo-Quiroz H, Keller JK, Zhuang Q (2013) Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales. Glob Change Biol 19(5):1325–1346CrossRefGoogle Scholar
  2. Brivio PA, Colombo R, Maggi M, Tomasoni R (2002) Integration of remote sensing data and GIS for accurate mapping of flooded areas. Int J Remote Sens 23(3):429–441. doi:10.1080/01431160010014729 CrossRefGoogle Scholar
  3. Cannon T (1994) Vulnerability analysis and the explanation of “natural” disasters. In: Varley A (ed) Disasters, development and the environment. John Wiley, ChichesterGoogle Scholar
  4. Cantisani A (2012) Monitoraggio e modellazione per la protezione dal rischio idraulico in aree pianeggianti mediante lo sviluppo e l’applicazione di modelli bidimensionali e l’utilizzo di strumenti GIS Open Source (Università della Basilicata)Google Scholar
  5. Cantisani A, Giosa L, Mancusi L, Sole A (2014) FLORA-2D: a new model to simulate the inundation in areas covered by flexible and rigid vegetation. Int J Eng Innov Technol 3(8):179–186Google Scholar
  6. Ceola S, Laio F, Montanari A (2014) Satellite nighttime lights reveal increasing human exposure to floods worldwide. Geophys Res Lett 41(20):7184–7190CrossRefGoogle Scholar
  7. Claps P, Fiorentino M (1999) Rapporto di sintesi sulla valutazione delle piene in Italia – Guida Operativa all’applicazione dei rapporti regionali sulla valutazione delle piene in Italia. Linea 1 Previsione e Prevenzione degli eventi idrologici estremi. CNR – GNDCI RomaGoogle Scholar
  8. Cobby DM, Mason DC, Davenport IJ (2001) Image processing of air born scanning laser altimetry for improved river flood modelling. ISPRS J Photogramm Remote Sens 56(2):121–138CrossRefGoogle Scholar
  9. De Risi R, Jalayer F, De Paola F, Giugni M (2014) Probabilistic delineation of flood-prone areas based on a digital elevation model and the extent of historical flooding: the case of Ouagadougou. Bol Geol Min 125(3):329–340Google Scholar
  10. Degiorgis M, Gnecco G, Gorni S, Roth G, Sanguineti M, Taramasso AC (2012) Classifiers for the detection of flood-prone areas using remote sensed elevation data. J Hydrol 470–471:302–315CrossRefGoogle Scholar
  11. Domeneghetti A, Tarpanelli A, Brocca L, Barbetta S, Moramarco T, Castellarin A, Brath A (2014) The use of remote sensing-derived water surface data for hydraulic model calibration. Remote Sens Environ 149(2014):130–141CrossRefGoogle Scholar
  12. Douglas I, Alam K, Maghenda M, Mcdonnell Y, Mclean L, Campbell J (2008) Unjust waters: climate change, flooding and the urban poor in Africa. Environ Urban 20:187. doi:10.1177/0956247808089156 CrossRefGoogle Scholar
  13. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874CrossRefGoogle Scholar
  14. Fiorentino M, Margiotta MR (1999) La valutazione dei volumi di piena ed il calcolo semplificato dell’effetto di laminazione di grandi invasi, Atti del 19° corso di aggiornamento su “Tecniche per la difesa dall’inquinamento, G. Frega (a cura di), Editoriale Bios, Cosenza, 203–222Google Scholar
  15. Fiorentino M, Manfreda S, Iacobellis V (2007) Peak runoff contributing area as hydrological signature of the probability distribution of floods. Adv Water Resour 30(10):2123–2134CrossRefGoogle Scholar
  16. Fluet-Chouinard E, Lehner B, Rebelo L-M, Papa F, Hamilton SK (2014) Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens Environ. doi:10.1016/j.rse.2014.10.015 Google Scholar
  17. Frappart F, Seyler F, Martinez J-M, León JG, Cazenave A (2005) Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels. Remote Sens Environ 99(4):387–399. doi:10.1016/j.rse.2005.08.016 CrossRefGoogle Scholar
  18. Freeman GE, Rahmeyer W, Copeland R R (2000) Determination of resistance due to shrubs and woody vegetation. Coastal and Hydraulics Laboratory, ERDC/CHL TR-00-25, U.S. Army EngineerGoogle Scholar
  19. Hjerdt KN, McDonnell JJ, Seibert J, Rodhe A (2004) A new topographic index to quantify downslope controls on local drainage. Water Resour Res 40:W05602Google Scholar
  20. Iacobellis V, Gioia A, Milella P, Satalino G, Balenzano A, Mattia F (2013) Inter-comparison of hydrological model simulations with time series of SAR-derived soil moisture maps. Eur J Remote Sens 46:739–757. doi:10.5721/EuJRS20134644 CrossRefGoogle Scholar
  21. Jalayer F, De Risi R, De Paola F et al (2014) Probabilistic GIS-based method for delineation of urban flooding risk hotspots. Nat Hazards. doi:10.1007/s11069-014-1119-2 Google Scholar
  22. Kirkby MJ (1975) Hydrograph modelling strategies. In: Peel R, Chisholm R, Haggett P (eds) Processes in physical and human geography. Heinemann, Oxford, pp 69–90Google Scholar
  23. Manfreda S, Di Leo M, Sole A (2011) Detection of flood prone areas using digital elevation models. J Hydrol Eng 16(10):781–790Google Scholar
  24. Manfreda S, Nardi F, Samela C, Grimaldi S, Taramasso AC, Roth G, Sole A (2014a) Investigation on the use of geomorphic approaches for the delineation of flood prone areas. J Hydrol 517:863–876CrossRefGoogle Scholar
  25. Manfreda S, Samela C, Sole A, Fiorentino M (2014b) Flood-prone areas assessment using linear binary classifiers based on morphological indices. Vulnerability, uncertain, and risk 2002–2011. doi:10.1061/9780784413609.201
  26. Medina V, Hurlimannn M, Bateman A (2008) Application of FLATModel, a 2D finite volume code, to debris flows in the northeastern part of Iberian Peninsula. Landslides 5(1):127–142Google Scholar
  27. Milly PCD, Wetherald RT, Dunne KA, Delworth TL (2002) Increasing risk of great floods in a changing climate. Nature 415:514–517CrossRefGoogle Scholar
  28. Nardi F, Vivoni ER, Grimaldi S (2006) Investigating a floodplain scaling relation using a hydrogeomorphic delineation method. Water Resour Res 42:W09409Google Scholar
  29. Nel JL, Roux DJ, Abell R, Ashton PJ, Cowling RM, Higgins JV, Thieme M, Viers JH (2009) Progress and challenges in freshwater conservation planning. Aquat Conserv Mar Fresh Water Ecosyst 19(4):474–485CrossRefGoogle Scholar
  30. O’Brien J (2007) FLO-2D user manual. Version 2007.06Google Scholar
  31. Papaioannou G, Vasiliades L, Loukas A (2014) Multi-criteria analysis framework for potential flood prone areas mapping. J Water Resour Manag. doi:10.1007/s11269-014-0817-6 Google Scholar
  32. Petryk S, Bosmajian GB (1975) Analysis of flow through vegetation. J Hydraul Div ASCE 101(7):871–884Google Scholar
  33. Prudhomme C, Reynard N, Crooks S (2002) Downscaling of global climate models for flood frequency analysis: where are we now? Hydrol Process 16:1137–1150CrossRefGoogle Scholar
  34. Sharitz RR, Mitsch WJ (1993) Southern hardwood forests. In: Martin WH, Boyce SG, Echtemacht AC (eds) Biodiversity of the southeastern United States: lowland terrestrial communities. Wiley, New York, pp 311–372Google Scholar
  35. Sole A, Giosa L, Cantisani A, Statuto D, Nolè L (2011) Analisi di sensibilità nella modellazione delle inondazioni di aree pianeggianti - Sensitivity analysis in flood modeling of flat areas. Ital J Eng Geol Environ 157–167. doi:10.4408/IERGE.2011-01.S-12
  36. Townsend PA, Walsh SJ (1998) Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing. Geomorphology 21(3–4):295–312. doi:10.1016/S0169-555X(97)00069-X CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Salvatore Manfreda
    • 1
  • Caterina Samela
    • 1
  • Andrea Gioia
    • 2
  • Giuseppe Gerardo Consoli
    • 1
  • Vito Iacobellis
    • 2
  • Luciana Giuzio
    • 1
  • Andrea Cantisani
    • 1
  • Aurelia Sole
    • 1
  1. 1.Università degli Studi della BasilicataPotenzaItaly
  2. 2.Politecnico di BariBariItaly

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