Encyclopedia of Coastal Science

Living Edition
| Editors: Charles W. Finkl, Christopher Makowski

Coastal Flood Hazard Mapping

  • Celene Milanés BatistaEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-48657-4_356-1



Coastal flood hazard mapping (CoFHaM) is a tool used to determine flood zone limits inland and in other areas exposed to coastal floods due to different hazards such as storm, surge waves, sea level rise caused by climate change, inland storm surge, heavy rainfall, among others. Its areas are identified through maps made by one or more researchers using the Geographic Information System (GIS) software. Generally, its maps display particular levels of flood hazards and risks, with different parameters defined as low, medium, and high.


Floods are among Earth’s most common and most destructive natural hazards (Nadeem et al. 2014; Gigović et al. 2017; Udin et al. 2018 p. 47). Most of these large floods occur in territories near the coast. The coastline represents an attractive zone for the establishment of various economic activities (IPCC 2007; Halpern et al. 2008). This singular space, over centuries, has been valued and has received high anthropic activity as well. The everyday presence of densely populated urban settlements located on the beachfront, raises concern in the scientific community, due to the different vulnerabilities and risks these communities endure. Coastal communities are increasingly experiencing climate change – induced coastal disasters and chronic flooding (Lipiec et al. 2018). These relevant coastal problems have led to the development of sophisticated coastal flood hazard mapping (CoFHaM) techniques in recent years.

Coastal flood hazard mapping (CoFHaM) has evolved over time. The first maps only presented static models of historic flood extensions as a guide for planners (Coller et al. 2018). In recent decades, coastal flood hazard mapping has been perfected with the introduction of computer modeling and mapping. These contemporary techniques offer a much greater power of cartographic representation, and they permit experts to evaluate a variety of possible events of floods in numerous places (Coller et al. 2018). The development achieved by the digital mapping of coastal flooding and the wide variety of models available today, which are generated through the use of computer systems, has also replaced the traditional use of paper maps. CoFHaM tools have also been perfected and diversified through the use and facilities provided by web mapping. Some of these websites are accessible and allow their users to share information about flood hazards (Coller et al. 2018). Examples of this service are provided by the environmental agency for the flood map service of England (Environment Agency 2013) and the National Flood Insurance Program (NFIP) in the U.S. Consumers can visit www.FloodSmart.gov to learn how to prepare for a flood, how to purchase a National Flood Insurance Policy, and how to access the benefits of protecting your home and property from flooding.

Coastal flood hazard mapping (CoFHaM) also originates as an instrument for territorial planning which, in a preventive way, guides urban planners and government officials in general. Furthermore, it helps them in the decision-making process when dictating insurance premiums which ideally reflect the way this risk is overseen. CoFHaM makes it possible to prepare answers in advance when facing emergency situations in areas where engineering protection is not practical. CoFHaM has begun to play an important role in raising public awareness of flood hazards (Coller et al. 2018).

Coastal flood hazard mapping tools are essential in flood risk assessment, as well as land use planning, supporting engineering decisions concerning flood control measures, and coordinating emergency responses (Coller et al. 2018). Modeling inland storm surge flooding is also a fundamental step in flood hazard mapping. As the primary tool for communicating flood exposure, flood maps quantify the spatial variation of flood effects (Hatzikyriakou and Ning 2017).

CoFHaM is used for a variety of potential situations such as hazard mapping impacts, vulnerabilities, and risks by means of land, air, and water, e.g., for structural vulnerability estimation (Hatzikyriakou and Lin 2017), flood risk analysis (FEMA 2017), and for offshore wind resource forecast. They are also used to broaden the understanding of diurnal coastal flows, to improve models for offshore wind power (Colle et al. 2016), and to access flooding potentialities in rivers located in large areas (Speckhann et al. 2018). Furthermore, CoFHaM is used to simulate storm surge waves, coastal flooding (AMA 2014 a, b; FEMA 2003a) and tsunami hazards (Priest et al. 2018); to map habitats and develop baselines in offshore marine reserves (Lawrence et al. 2015); and to undertake the mapping of the areas protected by Levee Systems (FEMA 2003b), as well as the urban coastal area (Gigović et al. 2017) and the coastal radiological hazards (Martin et al. 2018) among others.

Some researchers have undertaken some important theoretical discussions about flood hazard mapping in coastal zones. There are authors and institutions which analyze different scenarios and coastal limits where there is an incidence or direct impact of the sea towards the land surface, e.g., storm surge waves or sea level rise (FEMA 2003a; Hatzikyriakou and Ning 2017). Other scientists analyze the flood hazard mapping considering the river basin as a determining part of the coastal environment (Udin et al. 2018; Speckhann et al. 2018; Milanés et al. 2017, FEMA 2009). This second position is considered a holistic one, because it integrates two flood hazard scenarios: (1) the danger produced inland due to floods of rivers during their outfall into the sea and the combination of this scenario and the impact on its coastal limits after sea-flooding (see Fig. 1) and (2) the danger coastal floods produce on land due to sea-flooding or storm surge, as a result of a tropical storm, a hurricane of a different category or the gradual rise in sea level due to climate change (see Fig. 2).
Fig. 1

Schematic showing the forms of river flooding from its mouth to the sea. (a) Hydrographic basin under normal conditions; (b) overflowing river; (c) flooding due to river overflow in the section of the floodplain closest to the sea; (d) coastal flooding due to entrance of seawater after the effect of a hurricane, without river overflow; (e) flooded area due to hurricane and river overflow; and (f) flooded area due to hurricane combined with flooding due to river overflow in a section of the floodplain further inland

Fig. 2

(a) Model of coastal flood scenarios limits when facing category, I, III, and V hurricanes; (b) model of coastal flood scenarios by sea level rise due to climate change for the years 2050 and 2100; (c) the Peninsula of “Los Galeones” is projected to be most affected area by the floods, which, according to the combined hazard models for the year 2100, will convert it into a key

In Figs. 1 and 2, by using CoFHaM, a digital flood hazard database was created by compiling the best available data on flood-hazard areas and coastal risks; a systematic method to integrate coastal and riverine flood hazard areas was considered. In this case, coastal flood hazard boundary in land use planning was incorporated into the digital flood hazard database. Also, a combination of housing census data alongside the digital flood hazard database, using the geographic information system, satellite imagery, and digital photographs, was also employed.

The definition of coastal boundaries by nature of hazard is significant in the preparation of flood maps (Milanés 2018 a, b). Generally, each floodplain or flood hazard area is divided into flood insurance rates which are based on the floodplain boundaries determined on a work map (FEMA 2015, p. 1). Floodplain boundaries are delineated on the best available topographic mapping using the water-surface elevations determined at cross sections. Between cross sections, water surface elevations in riverine analyses are interpolated (FEMA 2015, p. 4).

In this entry, a critical review concerning different methods for coastal flood hazard mapping designed by relevant authors were shown. The materials, methods, and scales used are also described. Finally, two case studies using several methods and areas exposed to different coastal flood hazards in south-eastern Cuba are presented. In both case studies, coastal populations are exposed to different vulnerabilities and risks.

Methods and Tools Used for Coastal Flood Hazard Mapping (CoFHaM)

Each country determines the institutions which are responsible for centralizing risk maps. In the USA, flood mapping dictating insurance premiums which ideally reflect this risk is governed by the Federal Emergency Management Agency (FEMA) through its National Flood Insurance Program (NFIP) (Hatzikyriakou and Ning 2017; Crowell et al. 2007). FEMA prepares flood hazard mapping to create broad-based awareness of flood risk, provide data necessary for mitigation programs, and rate flood insurance for specific properties (FEMA 2015, p. 1). In Cuba, several institutions work in CoFHaM development, but the so-called “Defensa Civil” (The Cuban equivalent to FEMA) is the governing body which centralizes these studies and makes vital decisions.

Flood hazard mapping can be used for hydrodynamic models (Hatzikyriakou and Ning 2017; Dietrich et al. 2011). Other flood hazard mapping methodologies which combine flow frequency analysis with the height above the nearest drainage (HAND) model are applied in river basins. Its flood hazard mapping methodology, combines flow rate analysis of annual maxima with the HAND model (Speckhann et al. 2018), (see Fig. 3).
Fig. 3

Different methodological schemes for coastal flood hazard mapping

There are other tools of coastal flood hazard mapping which incorporate statistical analysis and meteorological modeling. There are others which use frequency curves over a range of potential rainfall and precipitation events (Coller et al. 2018). At times, the frequency analysis is also combined with the spatial modeling of the impact of floods on the curves of basic benefit costs (Morita 2008).

Figure 3 displays six methodological systems used by different authors for coastal flood hazard mapping. These tools were used in rural areas, river basins, coastal municipalities, and urban areas. A great difference is observed in the sequence of steps taken for these methodologies, which vary from three to eight stages according to their depth levels. Depending on the nature of the danger, Cuban methods differ in their ways of mapping floods; e.g., floods due to heavy rain (AMA 2014a) or due to sea floods (AMA 2014b). Other tools are used to separate the coastal and riverine zones and incorporate this new boundary line into the digital flood hazard database (Crowell et al. 2010).

Gigovi’c et al. (2017) considers six factors which are relevant to the hazard of flooding in urban areas: height, slope, distance to the sewage network, distance from the water surface, the water table, and land use. Expert evaluations take into account the nature and severity of the observed criteria; and these are tested using three scenarios: (1) the modalities of the analytical hierarchy process (AHP); (2) the use of the fuzzy technique for the exploitation of uncertainty with the AHP method, and (3) contemplate the use of the traditional AHP method (See Fig. 3).

In order to communicate flood exposure, FEMA developed flood insurance rate maps (FIRMs) which have the purpose of classifying coastal areas into flood zones based on two principal criteria. The first is the area’s base flood elevation (BFE), which represents the elevation of the estimated 100-year (i.e., 1% chance annually) flood event. The second criterion is the expected wave conditions associated with this event, specified as a range (e.g., “greater than 3 feet”). Another approach to evaluating FEMA’s flood hazard mapping for a location is to compare it with simulated inland flooding from a historical event (Hatzikyriakou and Ning 2017, p. 954).

Material, methods, and scales used for coastal flood hazard mapping are diverse. For the application of coastal flood hazard mapping (CoFHaM) Geographical Information Systems (GIS), historical data on floods, satellite imagery, base flood elevations, wave and tide gage data quadrangle maps showing the location, designation, and elevation of all high-water marks in hard copy and as a GIS spatial data coverage are used. Some hydrologic models, contour mapping or digital elevation models, topographic data, multicriteria decision analysis, assessment of experts, nature and severity of observed criteria can also be considered. The latter are tested using different scenarios or analytic hierarchy process technique as well as satellite photos or digital photographs of each high-water mark, of each coastal infrastructure (such as roads, bridges, culverts, or canals). Other flooding, risk, and vulnerabilities factors are also mapped.

Other methods use aerial photography and on-ground reports from NOAA and/or the media (namely, media reports from source such as the Internet, newspapers, radio, mobile, television, smart phone), information provided by local and state emergency managers, and other federal agencies (i.e., governmental situation reports, mathematical models intended for riverine flood analysis, diagnosis and preliminary damage assessments, experts criteria, preliminary damage assessments for hurricane events, telephone logs and brainstorms among specialists). The final results can be successfully used for spatial city development. The work scales can oscillate from 1:2000 to 1: 25,000 or higher depending on detailed studies. The smaller the scale the higher the levels of detail provided by the map.

On the other hand, the impact of models may change in accordance with and when compared to different models and countries. In the USA, areas within the one-percent-annual-chance (100-year) floodplain boundary are typically termed “Special Flood Hazard Areas,” and areas between the 1% and 0.2% annual chance (500-year) flood plain boundaries are termed areas of “Moderate Flood Hazard” (FEMA 2015) while in Cuba, areas that will be exposed to flooding by 2050 are considered moderate hazards, and areas already exposed since 2010 are in considerable danger.

Cuban Cases Studies

This section illustrates two case studies prepared in accordance with Cuban methodologies proposed by the Environment Agency of Cuba (AMA 2014 a, b). In the first case, flood scenarios are analyzed in the face of coastal hydrometeorological hazards combined with heavy rains, storm surge by category I, III, and V hurricanes, and sea level rise due to climate change from the 2050s up to the 2100 s. For the determination of assets and infrastructure exposed in the Primary Environmental Coastal Units for Integrated Management (PECUIM-Chivirico), located in the province of Santiago de Cuba, a multithreat and vulnerability analysis at a scale of 1:2000 was carried out. The models contemplate the vertical movements of the earth’s crust (Neotectonics of the Cuban archipelago).

The modeling of the flood scenario due to heavy rains was carried out following the calculations made by the provincial multidisciplinary group of hazard, vulnerability, and risk studies (CITMA 2013). The MapInfo version 12.0 Geographic Information System was used to obtain the flood areas for category I, III, and V hurricanes to also access the areas of Sea Level Rise due to Climate Change (SLRCC) and to generate all cartographic processing. For the raster format analysis, the Vertical Mapper module was used. Flooding areas were modeled considering the original values calculated by the Environmental Agency, which were validated after the passage of Hurricane Sandy through Santiago. The variables shown in Table 1 were incorporated into the studies.
Table 1

Calculation of the value of sea level rise (SLR)


Estimated for CUBA (CITMA 2013)

Equinox high tide


Final value of the SLR


27.0 cm

50.0 cm

0.9 cm

77.9 cm


85.0 cm

50.0 cm

0.9 cm

135.9 cm

Extreme floods through the years 2050 and 2100 s were calculated by the sum of the values of the SLRCC as a permanent flood scenario, and those obtained with the possible flood which may occur before a category V hurricane, (see Table 2).
Table 2

Calculation of the extreme SLRCC value at the PECUIM – Chivirico


Extreme situation with a category V Hurricane

Valor del SLR (m)

SLR value due to climate change (CC h)

Category V Hurricane (HCT – V)

SLR value due to CC + HCT – V









For the analysis of the impact of sea level rise due to climate change in the area under study, the southeastern block of Cuba rise of 1 cm per year is taken into account (IGP 2010) while there are sectors lowering 0.1 mm per year, as is the case of the coastal settlement of the PECUIM Chivirico (Arango 2014). For this research both results are taken into consideration; and therefore, the resulting rise of 0.9 mm per year is the value that is used. As a final result of the first case study, four flood maps are provided taking different hazards into consideration (see Fig. 4).
Fig. 4

(a) Area prone to flooding due to heavy rains in the PECUIM Chivirico; (b) area prone to flooding due to category I, III, and V hurricanes; (c) area prone to flooding as a consequence of sea level rise by 2050 and 2100 due to climate change; (d) map of coastal flood hazard areas due to storm surge in categories I, II, and V hurricanes, combined with heavy rains and sea level rise due to climate change by the year 2100

Fig. 5

(a) Flood hazard for a return period of 2 years; (b) Flood hazard for a return period of 10 years; (c) Flood hazard for a return period of 20 years (Source: Modified from Brito et al. 2013)

The second case study corresponds to the flood scenario due to heavy rains for the province of Santiago de Cuba, which is located in the southeastern region of Cuba. In this case, the analysis is made 1:25,000 scale in the 11 municipalities of the province, where only two of them are coastal (Santiago de Cuba and Guama).

For hazard mapping, with the aid of the GIS, the layers of susceptibility factors or the scenario of occurrence of the hazard are combined with the trigger factor layer, which in this case corresponds to the isoyetic maps made for the three periods of return evaluated (5%, 10%, and 50% of probability). These periods consider the maximum daily rainfall that occurs in 24 h. The classification of the rainfall factor intervals is shown in Table 3.
Table 3

Classification of the rainfall factor

Rain intervals in 24 h (mm)

Assigned value


Code on the map. Id

Greater than 401




Between 201 and 400




Less than 200




For each return period, a rain map is obtained. This map is linked to the scenario of occurrence of the hazard. The multiplication of the layers is carried out considering ranges and weights established in the development of the research, with those obtained in the classification of the intervals of the rain factor in Table 3. The results of this combination provide the intervals for the classification of the hazard in Table 4. Finally, from this classification, different flood hazard maps for the different return periods are drawn up (see Fig. 5).
Table 4

Classification of the areas according level of danger



Color on the map










The province of Santiago de Cuba has an area vulnerable to flooding due to heavy rains of 401.7 km2, which constitutes 6.5% of its total. Floods due to heavy rains occur mostly in municipalities with broad plains, low terrain and low permeable soils; among them, the two most affected and most extensive areas are the non-coastal municipalities of Mella and Contramaestre (Brito et al. 2013).

The floods in the coastal municipalities of Santiago de Cuba and Guama are smaller. These are produced mainly in the low areas and cones of dejection of the main rivers. In this area, the rivers have short courses and small basins. The inclination of its slopes accelerates the runoff caused by the large accumulation of water in the low areas and, consequently, leaves large amounts of soil and materials of various origins behind, which interrupt road traffic and isolate these territories.

For the return period of 2% (Fig. 5a), the dangers associated with heavy rains for most of the territory of the province of Santiago de Cuba display a behavior that oscillates between medium and low values. The northwestern zone of the Santiago de Cuba bay has a high risk of flooding due to the presence of low areas and the confluence of four rivers of great importance for the municipality.

For a return period of 10%, the hazards associated with heavy rains have a more homogeneous behavior (Fig. 5b). This allows a large part of the territory to have low danger values. The areas located in the northern portion of the province are the most extensive. The areas characterized as moderately dangerous are distributed punctually in different places of the province; although, in the central part, the greatest extension for this range of danger is observed.

Those areas with a high hazard level are reduced to smaller sectors and are highlighted mainly on coastal areas. In this sense, the dejection cones of the six rivers – namely, La Magdalena, La Plata, Uvero, Guama, Sigua, Baconao – stand out.

For a return period of 20%, the dangers associated with heavy rains have a more balanced behavior, which is characterized by low risk values in a large part of the territory (Fig. 5c). Those areas characterized with moderate danger are distributed only in small scattered sectors of the territory and mostly in the areas of the bay of Santiago de Cuba, (see Fig. 5d). The areas with a high danger are reduced to only three coastal sectors: Guamá, Sigua, and Baconao.


Coastal flood hazard mapping helps in analyzing different hazard scenarios in the coastal zone, coastal urban areas, and river basins. In the case studies integrated in the river basin, two different types of coastal floods are mapped which often occur in a combined way: (1) from the sea to land (when the river flows into the sea) and (2) from the land to the sea (when the sea makes its way into the mouth of the river).

Mapping methods and CoFHaM tools are available to help urban planners and decision-makers not only to make effective use of the data but also to provide new knowledge to minimize risks. Flood maps play an important role in raising awareness among the population and urban planners concerning the different types of flood hazards in order to lay the groundwork for preparing responses to emergency situations.

The two case studies analyzed were developed in the same coastal province of the Cuban archipelago. Results achieved allow us to conclude that the CoFHaM tool is effective to determine different types of hazards through the use of varied scales and levels of action (e.g., from province to municipality or locality) with scales from 1:2000 to 1:25,000.

The first case study analyzed in Cuba ratifies the vulnerability of the archipelago to the forecasts of the rise of the mean sea level due to climate change, where maximum values ​​of ascent of 0.59 m are proposed, for which adaptation and mitigation measures are needed for its confrontation. Furthermore, in the first case study analyzed, the hazards associated with coastal flooding due to combined hazards are classified into six parameters: extreme, very high, high, moderate, little, and low; while the results of the second case study of coastal flooding due to heavy rainfall in the different periods of return (5, 10, and 50 years) are based on four parameters: high, moderate, low, and no danger.



  1. AMA (2014a) Methodology for studies about danger, vulnerability and risk of disasters caused by heavy rain and floods In: Serrano et al (ed) Metodologías para la determinación de riesgos de desastres a nivel territorial. Parte 1. PNUD, Cuba, pp 11–32. ISBN: 978-959-300-033-8Google Scholar
  2. AMA (2014b) Methodology for studies about danger, vulnerability and risk of disasters caused by sea floods. In: Serrano et al (ed) Metodologías para la determinación de riesgos de desastres a nivel territorial. Parte 1. PNUD, Cuba, pp 35–51. ISBN: 978-959-300-033-8Google Scholar
  3. Arango Arias Enrique Diego (2014) Análisis sismotectónico del territorio oriental de Cuba a partir de la integración del modelo de corteza 3D de datos gravimétricos con datos sismológicos y geodésicos. Thesis presented for Degree of Doctor of Science. Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California, México, p 136Google Scholar
  4. Brito Ana Lourdes, Beyris Eduardo, Mirna (2013) Análisis del peligro por intensas lluvias para la provinica en la provinica de Santiago de Cuba. Estudio de PVR. CITMA, Santiago de Cuba, p 40Google Scholar
  5. CITMA (2013) Estudio de peligro vulnerabilidad y riesgo en la provincia de Santiago de Cuba. Delegación del Ministerio de Ciencia, Santiago de Cuba, p 65Google Scholar
  6. Colle BA, Sienkiewi MJ, Archer C, Veron D, Veron F, Kempton W, Mak JE (2016) Improving the mapping and prediction of offshore wind resources (IMPOWR). Experimental overview and first results. American Meteorological Society, pp 1377–1390.  https://doi.org/10.1175/BAMS-D-14-00253.1
  7. Coller MLF, Wheeler P, Kunapo J (2018) Interactive flood hazard visualization in Adobe Flash. J Flood Risk Manage 11(1):134–146CrossRefGoogle Scholar
  8. Crowell M, Edelman S, Coulton K, McAfee S (2007) How many people live in coastal areas? J Coast Res 23(4):iii–iviCrossRefGoogle Scholar
  9. Crowell M, Coulton K, Johnson C, Westcott J, Bellomo D, Edelman S, Hirsch E (2010) An estimate of the U.S. population living in 100-year coastal flood hazard areas. J Coast Res 26(2):201–211CrossRefGoogle Scholar
  10. Dietrich JC et al (2011) Modeling hurricane waves and storm surge using integrally-coupled, scalable computations. Coast Eng 58:45–65CrossRefGoogle Scholar
  11. Environment Agency (2013) Flood map for planning http://apps.environment-agency.gov.uk/wiyby/37837.aspx and flood map for planning risk http://apps.environment-agency.gov.uk/wiyby/cy/151263.aspx
  12. FEMA (2009) Guidelines and specifications for flood hazard mapping partners, appendix C: guidance for riverine flooding analyses and mapping. Federal Emergency Management Agency, Washington, DC, p 77Google Scholar
  13. FEMA (2015) Guidance for flood risk analysis and mapping. Riverine mapping and floodplain boundaries guidance. Guidance document 60, p 10Google Scholar
  14. FEMA 2017 Guidance for flood risk analysis and mapping flood insurance rate map (FIRM) database. Guidance document 36, p 48. www.fema.gov/guidelines-and-standards-flood-risk-analysis-and-mapping
  15. FEMA (Federal Emergency Management Agency) (2003a) Guidelines and specifications for flood hazard mapping partners, appendix D: guidance for coastal flooding analyses and mapping. Federal Emergency Management Agency, Washington, DC, p 177Google Scholar
  16. FEMA (Federal Emergency Management Agency) (2003b) Guidelines and specifications for flood hazard mapping partners. Appendix H guidance for mapping of areas protected by Levee Systems. Federal Emergency Management Agency, Washington, DC, p 16Google Scholar
  17. Gigović L, Pamuĉar D, Bajić Z, Drobnjak S (2017) Application of GIS-interval rough AHP methodology for flood hazard mapping in urban areas. Water 2017(9):360.  https://doi.org/10.3390/w9060360CrossRefGoogle Scholar
  18. Halpern BS, Walbridge S, Selkoe KA, Kappel CV, Micheli F (2008) A global map of human impact on marine ecosystems. Science 319:948–952.  https://doi.org/10.1126/science.1149345CrossRefGoogle Scholar
  19. Hatzikyriakou A, Ning L (2017) Simulating storm surge waves for structural vulnerability estimation and flood hazard mapping. Nat Hazards 89:939–962CrossRefGoogle Scholar
  20. IGP (Instituto de Geologia y Paleontología) (2010) Proyecto 4. Neotectonica. Resultados derivado del macroproyecto de “Escenarios de peligros y vulnerabilidad de la zona costera cubana asociados al ascenso del nivel medio del mar para los años 2050 y 2100”Google Scholar
  21. IPCC (2007) Intergovernmental pannel for climate change. IPCC fourth assessment report: climate change. Working group II: impacts, adaptation and vulnerability. https://www.ipcc.ch/publications_and_data/ar4/wg2/en/ch6s6-2-2.html
  22. Lawrence E, Hayes KR, Lucieer VL, Nichol SL, Dambacher JM, Hill NA (2015) Mapping habitats and developing baselines in offshore marine reserves with little prior knowledge: a critical evaluation of a new approach. PLoS One 10(10):0141051.  https://doi.org/10.1371/journal.pone.0141051CrossRefGoogle Scholar
  23. Lipiec E, Ruggiero P, Mills A, Serafin KA, Bolte J, Corcoran P, Stevenson J, Zanocco C, Lach D (2018) Mapping out climate change: assessing how coastal communities adapt using alternative future scenarios. J Coast Res.  https://doi.org/10.2112/JCOASTRES-D-17-00115.1
  24. Martin PG, Connor D, Payton OD, Leal-Olloqui M, Keatley AC, Scott TB (2018) Development and validation of a high-resolution mapping platform to aid in the public awareness of radiological hazards. J Radiol Prot 38:329–342CrossRefGoogle Scholar
  25. Milanes BC (2018a) Coastal boundaries. In: Finkl CW, Makowski C (eds) Encyclopedia of coastal science. Springer International Publishing AG.  https://doi.org/10.1007/978-3-319-48657-4_74-2
  26. Milanes BC (2018b) Coastal risk. In: Finkl CW, Makowski C (eds) Encyclopedia of coastal science. Springer International Publishing AG.  https://doi.org/10.1007/978-3-319-48657-4_408-1
  27. Milanés BC, Suérez A, Botero SC (2017) Novel method to delimitate and demarcate coastal zone boundaries. J Ocean Coast Manag 144:105–119. Available in http://www.sciencedirect.com/science/article/pii/S0964569117304155CrossRefGoogle Scholar
  28. Morita M (2008) Flood risk analysis for determining optimal flood protection levels in urban river management. J Flood Risk Manag 1:142.  https://doi.org/10.1111/j.1753-318X.2008.00016.xCrossRefGoogle Scholar
  29. Nadeem O, Jamshed A, Hameed R, Anjum GA, Khan A (2014) Post-flood rehabilitation of affected communities by NGOs in Punjab, Pakistan-learning lessons for future. J Fac Eng Technol 21(1):01–20Google Scholar
  30. Priest GR, Gabel LL, Wood NJ, Madin IP, Watzig RJ (2018) Correction to: beat-the-wave evacuation mapping for tsunami hazards in seaside, Oregon, USA. Nat Hazards 90:1509–1512CrossRefGoogle Scholar
  31. Speckhann AG, Borges CPL, Fabris GR, de Josina AJ, Altamirano FJA (2018) Flood hazard mapping in Southern Brazil: a combination of flow frequency analysis and the HAND model. Hydrol Sci J 63(1):87–100.  https://doi.org/10.1080/02626667.2017.1409896CrossRefGoogle Scholar
  32. Udin WS, Binti INA, Muchtar ABA, Muqtada AKM (2018) GIS-based River flood hazard mapping in rural area: a case study in Dabong, Kelantan, Peninsular Malaysia. Asian J Water Environ Pollut 15(1):47–55.  https://doi.org/10.3233/AJW-180005CrossRefGoogle Scholar
  33. Webpage https://www.floodsmart.gov/. Official website of the National Flood Insurance Program

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Research Group in Environmental Management and Sustainability, Department of Civil and EnvironmentalUniversidad de la Costa, BarranquillaAtlánticoColombia
  2. 2.Multidisciplinary Study Center of Coastal Zones (CEMZOC)Universidad de OrienteSantiago de CubaCuba