Hydrological and flood hazard assessment using a coupled modelling approach for a mountainous catchment in Portugal

  • A. R. Fonseca
  • M. Santos
  • J. A. Santos
Original Paper


Floods may lead to destruction of property, to damage to the environment and ultimately to loss of lives. Although it is not possible to avoid them, they are enhanced by human activities that increase the probability of occurrence of these natural events. Preliminary flood risk assessment and determination of areas of potential significant flood risk is mandatory according to the European Floods Directive (2007). In order to meet the established legislation, a methodology was developed that couples two modelling approaches: the Hydrological Simulation Program—FORTRAN (HSPF) and IBER. A target watershed, with complex orography and known to be vulnerable to flood hazards, is selected: the Vez River (northern Portugal). The performance of the HSPF model, driven by a climate gridded dataset, was assessed, followed by the reconstruction of the flow rate in the catchment for the period from 1950 to 2015. The results hint at an agreement between simulated and observed daily flow rates, with high coefficient of determination value and of the Nash–Sutcliffe coefficient of efficiency (> 0.7 daily timescale). A satisfactory performance was also found in reproducing flood peak events. An average deviation of 10% was found between observed and simulated flood peaks. The output of HSPF was subsequently used to drive IBER, thus determining flood hazard areas for a 10, 50 and 100-year return periods. The methodology presented here provides basic tools for decision-makers to evaluate hydrologic responses to climate data, namely the determination of flood hazard maps, but also risk assessment, water management, environmental protection and sustainability.


Hydrological modelling Flood hazard HSPF IBER Vez catchment Portugal 



This study was funded by the project INTERACT—Integrative Research in Environment, Agro-Chain and Technology, in its research line BEST, NORTE-01-0145-FEDER-000017, co-funded by FEDER/NORTE 2020 (Programa Operacional Regional do Norte 2014/2020). It was also supported by FEDER/COMPETE/POCI – Operational Competitiveness and Internationalization Programme, POCI-01-0145-FEDER-006958, and by FCT—Portuguese Foundation for Science and Technology, UID/AGR/04033/2013.

Supplementary material

477_2018_1525_MOESM1_ESM.docx (3.8 mb)
Supplementary material 1 (DOCX 3845 kb)


  1. Alexandre Diogo P, Nunes JP, Carmona Rodrigues A, et al (2014) Hydropower and water supply: competing water uses under a future drier climate modeling scenarios for the Tagus River basin, Portugal. In: EGU general assembly conference abstractsGoogle Scholar
  2. Ames DP, Michaelis C, Anselmo A, et al (2008) MapWindow GIS. In: Encyclopedia of GIS. Springer, pp 633–634Google Scholar
  3. Belo-Pereira M, Dutra E, Viterbo P (2011) Evaluation of global precipitation data sets over the Iberian Peninsula. J Geophys Res Atmos. CrossRefGoogle Scholar
  4. Bergman MJ, Green W, Donnangelo LJ (2002) Calibration of storm loads in the south Prong watershed, Florida, using BASINS/HSPF, pp 1423–1436Google Scholar
  5. Bicknell BR (2000) Basins technical note 6: estimating hydrology and hydraulic parameters for HSPF. US: Environmental Protection AgencyGoogle Scholar
  6. Bicknell BR, Imhoff JC, Kittle JL Jr et al (2001) Hydrological Simulation Program—FORTRAN: HSPF version 12 user’s manual. AQUA TERRA Consultants, Mountain ViewGoogle Scholar
  7. Bladé E, Cea L, Corestein G et al (2014) IBER: herramienta de simulación numérica del flujo en ríos. Rev Int Métodos Numéricos para Cálculo y Diseño en Ing 30:1–10. CrossRefGoogle Scholar
  8. Bleecker M, DeGloria S, Hutson J et al (1995) Mapping atrazine leaching potential with integrated environmental databases and simulation models. J Soil Water Conserv 50:388–394Google Scholar
  9. Bodoque JM, Amérigo M, Díez-Herrero A et al (2016) Improvement of resilience of urban areas by integrating social perception in flash-flood risk management. J Hydrol 541:665–676. CrossRefGoogle Scholar
  10. Brandt SA (2016) Modeling and visualizing uncertainties of flood boundary delineation: algorithm for slope and DEM resolution dependencies of 1D hydraulic models. Stoch Environ Res Risk Assess 30:1677–1690. CrossRefGoogle Scholar
  11. Carrubba L (2000) Hydrologic modeling at the watershed scale using NPSM. J Am Water Resour Assoc 36:1237–1246CrossRefGoogle Scholar
  12. Carvalho-Santos C, Nunes J, Hein L, Honrado J (2016) Modelling hydrological services using SWAT—impacts from forestation scenarios in a transitional Mediterranean climatic watershed. In: 6th ESP annual international conference: making ecosystems services count, Bali, pp 26–30Google Scholar
  13. Castillo C, Pérez R, Gómez JA (2014) A conceptual model of check dam hydraulics for gully control: efficiency, optimal spacing and relation with step-pools. Hydrol Earth Syst Sci 18:1705–1721. CrossRefGoogle Scholar
  14. Conradt T, Roers M, Schroeter K et al (2013) Comparison of the extreme floods of 2002 and 2013 in the German part of the Elbe River basin and their runoff simulation by SWIM-live. Hydrol Und Wasserbewirtschaftung 57:241–245Google Scholar
  15. Crawford N (1966) Digital simulation in hydrology: stanford watershed model IV. Stanford University, Technical Report, 39Google Scholar
  16. de Moel H, van Alphen J, Aerts J (2009) Flood maps in Europe-methods, availability and use. Nat Hazards Earth Syst Sci 9:289–301CrossRefGoogle Scholar
  17. Directive (2007) Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risksGoogle Scholar
  18. Donigian AS (2002) Watershed model calibration and validation: the HSPF experience. Proc Water Environ Fed 2002:44–73CrossRefGoogle Scholar
  19. Donigian AS, Crawford NH (1976) Modeling nonpoint pollution from the land surface. US Environmental Protection Agency, Office of Research and Development, Environmental Research LaboratoryGoogle Scholar
  20. Donigian AS, Davis HH (1978) Agricultural runoff management (ARM): users manual. Report EPA–600/3–78–080, US EPA, Athens, Georgia, USAGoogle Scholar
  21. Donigian AS, Huber WC (1991) Modeling of nonpoint source water quality in urban and non-urban areas. Environmental Research Laboratory, Office of Research and Development, US Environmental Protection AgencyGoogle Scholar
  22. Donigian AS, Bicknell BR, Imhoff JC (1995) Hydrological simulation program—Fortran (HSPF). Comput Model watershed Hydrol 12:395–442Google Scholar
  23. EPA U (2015) BASINS 4.1 (better assessment science integrating point & non-point sources) modeling frameworkGoogle Scholar
  24. Ferreira ARL, Fernandes LFS, Cortes RMV, Pacheco FAL (2017) Assessing anthropogenic impacts on riverine ecosystems using nested partial least squares regression. Sci Total Environ 583:466–477CrossRefGoogle Scholar
  25. Fonseca A, Ames DP, Yang P et al (2014a) Watershed model parameter estimation and uncertainty in data-limited environments. Environ Model Softw 51:84–93. CrossRefGoogle Scholar
  26. Fonseca A, Botelho C, Boaventura RAR, Vilar VJP (2014b) Integrated hydrological and water quality model for river management: a case study on Lena River. Sci Total Environ 485:474–489. CrossRefGoogle Scholar
  27. Fonseca A, Botelho C, Boaventura RAR, Vilar VJP (2015) Global warming effects on faecal coliform bacterium watershed impairments in Portugal. River Res Appl 31:1344–1353. CrossRefGoogle Scholar
  28. Fonseca AR, Sanches Fernandes LF, Fontainhas-Fernandes A et al (2016) From catchment to fish: impact of anthropogenic pressures on gill histopathology. Sci Total Environ 550:972–986. CrossRefGoogle Scholar
  29. Fonseca AR, Fernandes LFS, Fontainhas-Fernandes A et al (2017) The impact of freshwater metal concentrations on the severity of histopathological changes in fish gills: a statistical perspective. Sci Total Environ 599:217–226CrossRefGoogle Scholar
  30. Fonseca A, Boaventura RA, Vilar VJ (2018) Integrating water quality responses to best management practices in Portugal. Environ Sci Pollut Res 25(2):1587–1596CrossRefGoogle Scholar
  31. Garrote J, Alvarenga FM, Díez-Herrero A (2016) Quantification of flash flood economic risk using ultra-detailed stage–damage functions and 2-D hydraulic models. J Hydrol 541:611–625CrossRefGoogle Scholar
  32. Hävermark S (2016) Modelling the effects of land use change on a peri-urban catchment in PortugalGoogle Scholar
  33. Haylock M, Hofstra N, Klein Tank A et al (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res Atmos. CrossRefGoogle Scholar
  34. Hughes DA, Slaughter A (2015) Daily disaggregation of simulated monthly flows using different rainfall datasets in southern Africa. J Hydrol Reg Stud 4:153–171CrossRefGoogle Scholar
  35. Hummel PR, Kittle Jr JL, Gray MH (2001) WDMUtil-A tool for managing watershed modeling time-series data: user’s manual. US EPA Office of Water, Washington, DCGoogle Scholar
  36. Huza J, Teuling AJ, Braud I et al (2014) Precipitation, soil moisture and runoff variability in a small river catchment (Ardèche, France) during HyMeX Special Observation Period 1. J Hydrol 516:330–342. CrossRefGoogle Scholar
  37. Jayawardena AW (2015) Hydro-meteorological disasters: causes, effects and mitigation measures with special reference to early warning with data driven approaches of forecasting. Proc IUTAM 17:3–12CrossRefGoogle Scholar
  38. Kilsby CG, Tellier SS, Fowler HJ, Howels TR (2007) Hydrological impacts of climate change on the Tejo and Guadiana Rivers. Hydrol Earth Syst Sci Discuss 11:1175–1189CrossRefGoogle Scholar
  39. Kohler MA, Nordenson T, Fox W (1955) Evaporation from pans and lakes: US weather bureau research paper 38Google Scholar
  40. Kouwen N, Danard M, Bingeman A et al (2005) Case study: watershed modeling with distributed weather model data. J Hydrol Eng 10:23–38CrossRefGoogle Scholar
  41. Kron W, Steuer M, Löw P, Wirtz A (2012) How to deal properly with a natural catastrophe database – analysis of flood losses. Nat Hazards Earth Syst Sci 12:535–550. CrossRefGoogle Scholar
  42. Lian Y, Chan I-C, Singh J et al (2007) Coupling of hydrologic and hydraulic models for the Illinois River Basin. J Hydrol 344:210–222CrossRefGoogle Scholar
  43. Liu R, Chen Y, Wu J et al (2016) Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: a case study in Bowen Basin, Australia. Stoch Environ Res risk Assess 30:1575–1590. CrossRefGoogle Scholar
  44. López-Moreno JI, Vicente-Serrano SM, Beguería S, et al (2009) Dam effects on droughts magnitude and duration in a transboundary basin: The Lower River Tagus, Spain and PortugalGoogle Scholar
  45. Lowe SA, Doscher R (2003) Modeling of urban watersheds using basins and HSPF. J Environ Hydrol 11Google Scholar
  46. Mehta VK, Walter MT, Brooks ES et al (2004) Application of SMR to modeling watersheds in the Catskill Mountains. Environ Model Assess 9:77–89CrossRefGoogle Scholar
  47. Mendes MP, Ribeiro L, Nascimento J et al (2012) A groundwater perspective on the river basin management plan for central Portugal—developing a methodology to assess the potential impact of N fertilizers on groundwater bodies. Water Sci Technol 66:2162–2169CrossRefGoogle Scholar
  48. Merz R, Blöschl G, Humer G (2008) National flood discharge mapping in Austria. Nat Hazards 46:53–72. CrossRefGoogle Scholar
  49. Merz B, Aerts J, Arnbjerg-Nielsen K et al (2014) Floods and climate: emerging perspectives for flood risk assessment and management. Nat Hazards Earth Syst Sci 14:1921–1942. CrossRefGoogle Scholar
  50. Mourato S, Moreira M, Corte-Real J (2014) Water availability in southern Portugal for different climate change scenarios subjected to bias correction. J Urban Environ Eng 8(1):109–117Google Scholar
  51. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  52. Nied M, Pardowitz T, Nissen K et al (2014) On the relationship between hydro-meteorological patterns and flood types. J Hydrol 519:3249–3262. CrossRefGoogle Scholar
  53. Nied M, Schröter K, Lüdtke S et al (2017) What are the hydro-meteorological controls on flood characteristics? J Hydrol 545:310–326CrossRefGoogle Scholar
  54. O’Neill BC, Oppenheimer M, Warren R et al (2017) IPCC reasons for concern regarding climate change risks. Nat Clim Chang 7:28–37CrossRefGoogle Scholar
  55. Obled C, Wendling J, Beven K (1994) The sensitivity of hydrological models to spatial rainfall patterns: an evaluation using observed data. J Hydrol 159:305–333CrossRefGoogle Scholar
  56. Palmer MD (1981) Some measurements of near surface turbulence in the depth direction and some phytoplankton distribution implications. J Great Lakes Res 7:171–181CrossRefGoogle Scholar
  57. Palmer MD (2001) Water quality modeling: a guide to effective practice. World bank publications, Washington, DCCrossRefGoogle Scholar
  58. Pathiraja S, Westra S, Sharma A (2012) Why continuous simulation? The role of antecedent moisture in design flood estimation. Water Resour Res 48(6).
  59. Penman HL (1948) Natural evaporation from open water, bare soil and grass. In: Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences. The Royal Society, pp 120–145Google Scholar
  60. Petan S, Barbosa JLP, Mikos M, Pinto FT (2009) GIS-based RUSLE modelling of Leça River Basin, Northern Portugal, in two different grid scales. In: EGU general assembly conference abstracts, p 9334Google Scholar
  61. Reis A, Martinho Lourenço JM, Parker A, Alencoão A (2013) Evaluation of soil erosion as a basis of sediment yield in mountainous catchments: a preliminary study in the River Douro Basin (Northern Portugal). In: EGU general assembly conference abstractsGoogle Scholar
  62. Riccardi GA (1997) The mathematical modelling of flood propagation for the delineation of flood risk zones. IAHS Publ Proc Reports-Intern Assoc Hydrol Sci 240:355–364Google Scholar
  63. Ruiz-Villanueva V, Bladé E, Sánchez-Juny M et al (2014) Two-dimensional numerical modeling of wood transport. J Hydroinformatics 16:1077–1096CrossRefGoogle Scholar
  64. Santos PP, Reis E (2017) Assessment of stream flood susceptibility: a cross analysis between model results and flood losses. J Flood Risk Management. CrossRefGoogle Scholar
  65. Santos M, Santos JA, Fragoso M (2015a) Historical damaging flood records for 1871–2011 in northern Portugal and underlying atmospheric forcings. J Hydrol 530:591–603CrossRefGoogle Scholar
  66. Santos RMB, Fernandes LFS, Pereira MG et al (2015b) Water resources planning for a river basin with recurrent wildfires. Sci Total Environ 526:1–13CrossRefGoogle Scholar
  67. Santos M, Fragoso M, Santos JA (2017a) Regionalization and susceptibility assessment to daily precipitation extremes in mainland Portugal. Appl Geogr 86:128–138CrossRefGoogle Scholar
  68. Santos M, Santos JA, Fragoso M (2017b) Atmospheric driving mechanisms of flash floods in Portugal. Int J Climatol. CrossRefGoogle Scholar
  69. Schröter K, Kunz M, Elmer F et al (2015) What made the June 2013 flood in Germany an exceptional event? A hydro-meteorological evaluation. Hydrol Earth Syst Sci 19:309–327. CrossRefGoogle Scholar
  70. Schuol J, Abbaspour K (2007) Using monthly weather statistics to generate daily data in a SWAT model application to West Africa. Ecol Modell 201:301–311CrossRefGoogle Scholar
  71. Shrestha R, Tachikawa Y, Takara K (2004) Performance analysis of different meteorological data and resolutions using MaScOD hydrological model. Hydrol Process 18:3169–3187CrossRefGoogle Scholar
  72. Silva E, Pereira AC, Estalagem SP et al (2012) Assessing the quality of freshwaters in a protected area within the Tagus river basin district (central Portugal). J Environ Qual 41:1413–1426CrossRefGoogle Scholar
  73. Te Chow V (1959) Open channel hydraulics. McGraw-Hill Book Company, Inc, New YorkGoogle Scholar
  74. Ulbrich U, Brücher T, Fink AH et al (2003a) The central European floods of August 2002: part 1—rainfall periods and flood development. Weather 58:371–377. CrossRefGoogle Scholar
  75. Ulbrich U, Brücher T, Fink AH et al (2003b) The central European floods of August 2002: part 2—synoptic causes and considerations with respect to climatic change. Weather 58:434–442. CrossRefGoogle Scholar
  76. Versteeg HK, Malalasekera W (2007) An introduction to computational fluid dynamics: the finite method. Pearson Education, LondonGoogle Scholar
  77. Vieira J, Fonseca A, Vilar VJP et al (2012) Water quality in Lis river, Portugal. Environ Monit Assess 184:7125–7140. CrossRefGoogle Scholar
  78. Vieira J, Fonseca A, Vilar VJP et al (2013) Water quality modelling of Lis River, Portugal. Environ Sci Pollut Res 20:508–524. CrossRefGoogle Scholar
  79. Wahren F, Julich S, Nunes J et al (2016) Combining digital soil mapping and hydrological modeling in a data scarce watershed in north-central Portugal. Geoderma 264:350–362CrossRefGoogle Scholar
  80. Yang P, Ames DP, Fonseca A et al (2014a) What is the effect of LiDAR-derived DEM resolution on large-scale watershed model results? Environ Model Softw 58:48–57. CrossRefGoogle Scholar
  81. Yang P, Ames DP, Fonseca A, et al (2014b) Impact of LiDAR-derived DEM resolution on hydrographic features and hydrologic modeling. In: Proceedings—7th international congress on environmental modelling and software: bold visions for environmental modeling, iEMSs 2014Google Scholar
  82. Zhang J, Ross M, Trout K, Zhou D (2009) Calibration of the HSPF model with a new coupled FTABLE generation method. Prog Nat Sci 19:1747–1755CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centre for the Research and Technology of Agro-environmental and Biological Sciences, CITABUniversidade de Trás-os-Montes e Alto Douro, UTADVila RealPortugal
  2. 2.Institute of Geography and Spatial Planning, Edifício IGOTUniversidade de LisboaLisbonPortugal

Personalised recommendations