Elsevier

Continental Shelf Research

Volume 228, 1 October 2021, 104489
Continental Shelf Research

Costal sea level variability and extreme events in Moñitos, Cordoba, Colombian Caribbean Sea

https://doi.org/10.1016/j.csr.2021.104489Get rights and content

Highlights

  • Total sea level variability on the Moñitos coast was studied for different time scales.

  • Wave run-up dominates Total Sea Level for short-term time scales (up to 2 years).

  • Altimetric sea level dominates TSL variability for long-term time scales (>7 years).

  • In extreme conditions of TSL, the astronomical tide is an important contributor.

  • The greatest impact of ENSO on Total Sea Level occurs with a five-month lag.

Abstract

Flooding episodes occur frequently in the Colombian Caribbean and cause damage to coastal settlements. However, there is little knowledge about these episodes and about how changes in associated variables affect coastal flooding. This paper presents the results obtained from analyzing the temporal variability of flood levels in Moñitos-Córdoba on the Colombian Caribbean coast, as well as the contribution their components make to different time scales. To achieve this, the total sea level (TSL) was estimated indirectly as the sum of the variables involved (sea level anomalies, astronomical tide, storm surge and wave runup). These variables were obtained by applying numerical and empirical modeling using satellite altimetry data, tidal modeled data and wind, waves and atmospheric pressure from reanalysis. Data Trends and contributions were analyzed using statistical methods, including variance analysis, exceedance distributions, linear regressions, Sen-Slope and the Mann Kendall test. The results indicate that the total sea level has a semi-annual cycle with its highest maximum levels in the months of December to March and its lowest maximum levels in the months of April and September. The total sea level variability in Moñitos is dominated by the runup component at monthly, intra-annual and inter-annual scales, while at longer-term timescales (2–7 years and greater) variability is dominated by the sea level anomaly. Runup is the greatest contributor to the total sea level, followed by the sea level anomaly in average conditions and the astronomical tide in extreme conditions. There was a trend of increasing total sea level, related to the trend in sea level anomaly, with a consequent increasing trend of frequency and magnitude of extreme sea levels.

Introduction

Scientific evidence indicates that the planet is warming and sea levels are rising (Shaftel et al., 2018), and this is expected to continue to occur despite a certain degree of uncertainty around trends (Le Cozannet et al., 2015). Studies based on climate models have found that the mean sea level could increase by up to 1 m or more by the year 2100 (Nicholls et al., 2014). The impact of sea level rise is expected to become more severe. Episodes of coastal flooding will likely increase due to the combination of sea level rise and variations in extreme weather events (Losada et al., 2013). This behavior will severely affect coastal settlements.

Coastal water levels and their variability result from multiple processes occurring on different time and space scales and their respective interactions. Chelton and Enfield (1986) identify nine processes that contribute to sea level variability: astronomical tides, the inverse barometer effect, geostrophic currents, coastal upwelling, coastal trapped waves, seasonal variability, low-frequency atmospheric forcing, the El Niño–Southern Oscillation (ENSO) phenomenon and secular variability. According to Chen et al. (2010), at a global scale sea level variability presents eleven main modes which can be classified into four regimes: seasonal (3 and 6 months), annual (12 months), inter-annual (1.55, 1.74, 1.94, 2.34, 3.07, 4.20 and 5.40 years) and decadal (9.28 years). Despite this classification, geographical variability depends on the local characteristics of the phenomena that contribute to coastal sea levels, such as wave conditions, winds, pressure or tidal range. These can be caused by different combinations of phenomena, such as storm surge conditions that occur often (Breilh et al., 2014; Cid et al., 2017).

About interannual variability of sea level, Valle-Levinson and Martin (2020), studied the influence of lunar precessions and solar activity periodicities in the moments of rapid sea level rise with periods greater than 5 years (referred as hot moments). The results show a high statistical explanation for maximum values of sea level rise when several harmonics are employed to reconstruct the signal for the coastal areas of the United States and the Gulf of Mexico. Other authors such as (Kristjansson et al., 2002; Barripedro et al., 2010; Martinez-Asencio et al., 2016, Kaniewski et al., 2016, among others) have shown the connection between this astronomical forcing and other physical processes affecting extreme sea level events, such as winds, tides, atmospheric pressure, daytime temperatures and cloud cover.

Coastal flooding is caused by a combination of different processes superimposed on global, regional and local scales. According to Rueda et al. (2017), in most coastal regions, floods are mainly dominated by the astronomical tide, with a 59% average global contribution. This is followed by wave set-up and storm surge, with average global contributions of 29% and 12% respectively. According to Melet et al. (2018), contributions related to waves (Setup and swash) are the most significant on interannual-to-multidecadal scales. Other authors have also reported on the importance of components associated with waves (Dada et al., 2020; Serafin et al., 2017; Wadey et al., 2017). Likewise, coasts have been identified where there are significant contributions from fluctuations in the mean sea level (Eliot, 2012). Therefore, although meteorological and oceanographic processes occur on a global scale, they have a diverse impact on the variability of sea level on both a temporal scale and on a spatial scale, with differences from one coast to another (Melet et al., 2018; Rueda et al., 2017).

In the Colombian Caribbean, the importance of how the different oceanographic processes contribute to coastal flooding is not well understood, and neither are the effects of spatial scales on the coastal sea level. This represents challenges for planning the development of coastal settlements and incorporating risk management of coastal flooding, for which an understanding of the variability and forcing factors is essential. Examples of the few studies carried out on the subject are those of Martínez (2010), who looked at several representative sites and proposed a methodology to estimate flood levels on the Colombian Caribbean coast, Losada et al. (2013), who studied sea level, its components and long-term trends for Latin American and Caribbean coasts, however with coarse resolutions. There are also, some local studies like Andrade et al. (2013) and Nicolae et al. (2008), who assessed flooding in Cartagena due to extreme events ('Mares de leva'), and Osorio et al. (2014) who evaluated the flood level at Playa Palmeras in the Colombian Pacific. More recently, research has been carried out by Orejarena-Rondón et al. (2018), who present a methodology for obtaining sea level regimes in microtidal areas with scarce data availability and apply it to Cartagena and the Gulf of Urabá, and Orejarena-Rondón et al. (2019), who studied the combined impact of extreme waves and sea level in Bocagrande, Cartagena. In these studies, coastal flooding was addressed using numerical models and reanalysis data due to the scarcity of measured data. However, climatic analysis and its relationship with macroclimatic phenomena or extreme events on several scales has not been studied in detail. Challenges related with long term changes in sea level rise, wave energy fluxes and atmospheric patterns as a consequence of climate change and climate variability should be explored on local scales, as affirmed by several authors (Casas-Prat et al., 2018; Mentaschi et al., 2017; Orejarena-Rondón et al., 2019).

In the literature, the study of coastal flooding has been addressed in various ways depending on the purpose, scale of interest and available data. Some are based on hydrodynamic models (Fernández et al., 2018; Yin et al., 2017) and 'static' approaches where flooding for each temporal scenario is obtained from the 'Total Water Level’ – TWL (Serafin and Ruggiero, 2014; Wang et al., 2017). In the latter approach, the different components of the flood are simulated independently through numerical models (Losada et al., 2013) and/or using empirical parameterizations for variables such as runup (Christie et al., 2017; Silva et al., 2017) and storm surge (Del Río et al., 2012; Maia et al., 2016). Some applications of the TWL estimate have also been performed using observational data such as in-situ wave buoy and tide gauge data (Serafin and Ruggiero, 2014). In coastal and oceanic areas of Colombia, marine climate information is scarce or is of low quality (Ortiz et al., 2014; Osorio et al., 2016). Conventional instrumentation such as wave buoys or tide gauges tend to be limited to certain strategic points like important ports or tourist areas, e.g. Cartagena and San Andrés. This limits the ability of scientists to study coastal flooding, which is why such studies are scarce in Colombia.

The aim of this paper is to analyze the behavior of the coastal sea level variability in Moñitos, in the municipality of Córdoba using the ‘Total Sea Level (TSL)’ approach. Variability at different time scales, the occurrence of extreme events, the relative contribution of different components and the incidence of the ENSO phenomena were studied. It is expected that this study will serve as a methodological proposal for the understanding of these processes in the other coastal regions in the great Caribbean and the Antilles islands. This paper is organized as follows: Section 2 presents a description of the study area, Section 3 gives a description of the materials and methods employed, Section 4 presents the results and discussion of flood variability, and conclusions are given in Section 5.

Section snippets

Study area

The Moñitos municipality is located on the Colombian Caribbean coast, in the vicinity of the Gulf of Morrosquillo (Fig. 1). The urban area of the municipality covers 22.9 Km2, has a population of 6,454 inhabitants, and is located on a coastal plain adjacent to the coastline. The whole plain is affected by episodes of coastal flooding and the regional climate cycle is mainly determined by the latitudinal migration of the Intertropical Convergence Zone -ZCIT, the Northeast Trade Winds and the

Materials and methods

The flooding elevation or total sea level at the coast (TSL), which vary for each time (six hourly frequency, 0, 6, 12, 12 UTC), was obtained as the sum of the contributing factors, as presented by several authors such as, Serafin and Ruggiero (2014), Villatoro et al. (2014), Melo et al. (2016), Melo et al. (2018), Hakkou et al. (2019), Serafin et al. (2017), Data et al. (2020), among others. For this approach, the TSL is estimated as follows.TSL=SLA+AT+SS+RuWhere SLA is the altimetry derived

Time series analysis

The six hourly data series of the variables contributing to the Total Sea Level (TSL) for the period 1993–2015 are shown in Fig. 8. The results show that the sea level anomaly series (SLA; Fig. 8a), astronomical tide series (AT; Fig. 8b) and storm surge (SS, Fig. 8c - gray lines) present values between −0.08 and 0.22 m, −0.19 m–0.29 m and 0.1 m–0.23 m, respectively. The mean and standard deviations are 0.04 m and 0.05 for SLA, 0 and 0.1 m for AT and 0 and 0.02 m for SS respectively. This agrees

Conclusions

In order to generate information to support decision-making in local planning, the variability in total sea level (TSL) on the coast of Moñitos, Córdoba, was analyzed. The significance of the different associated processes that cause variations on different time scales was also investigated. To achieve this, the total sea level (TSL) was modeled for the time window 1993 to 2015. The main findings are as follows:

In mean conditions, total sea level (TSL) in Moñitos has a semi-annual cycle with

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References (155)

  • G. Chen et al.

    Seasonal-to-decadal modes of global sea level variability derived from merged altimeter data

    Rem. Sens. Environ.

    (2010)
  • A. Cid et al.

    Global reconstructed daily surge levels from the 20th Century Reanalysis (1871–2010)

    Global Planet. Change

    (2017)
  • O.A. Dada et al.

    Recent coastal sea-level variations and flooding events in the Nigerian Transgressive Mud coast of Gulf of Guinea

    J. Afr. Earth Sci.

    (2020)
  • L. Del Río et al.

    Establishing storm thresholds for the Spanish Gulf of Cádiz coast

    Geomorphology

    (15 March 2012)
  • A. Devis-Morales et al.

    Assessment of extreme wind and waves in the Colombian Caribbean Sea for offshore applications

    Appl. Ocean Res.

    (2017)
  • M. Eliot

    Sea level variability influencing coastal flooding in the Swan River region, Western Australia

    Continent. Shelf Res.

    (2012)
  • V. Fernández et al.

    Coastal flood assessment due to extreme events at Ensenada, Baja California, Mexico

    Ocean Coast Manag.

    (2018)
  • Y. Fu et al.

    Estimation of sea level variability in the South China Sea from satellite altimetry and tide gauge data

    Adv. Space Res.

    (2021)
  • M. Hakkou et al.

    Assess and mapping the flooding hazards using geospatial tools and empirical model along Kenitra coast, Morocco

    Ocean Coast Manag.

    (2019)
  • J.R. Hunter et al.

    Towards a global regionally varying allowance for sea-level rise

    Ocean. Eng.

    (2013)
  • H.D. Ibrahim et al.

    Mechanism study of the 2010–2016 rapid rise of the Caribbean Sea level

    Global Planet. Change

    (2020)
  • P.A. Janssen

    Progress in ocean wave forecasting

    J. Comput. Phys.

    (2008)
  • N. Keshta et al.

    Utilizing North American regional reanalysis for modeling soil moisture and evapotranspiration in reconstructed watersheds

    Phys. Chem. Earth, Parts A/B/C

    (2011)
  • G. Le Cozannet et al.

    Evaluating uncertainties of future marine flooding occurrence as sea-level rises

    Environ. Model. Software

    (2015)
  • W.C. Liu et al.

    Influences of sea level rise on tides and storm surges around the Taiwan coast

    Continent. Shelf Res.

    (2019)
  • J. Li et al.

    An extreme sea level event along the northwest coast of the South China sea in 2011–2012

    Continent. Shelf Res.

    (2020)
  • B. Liang et al.

    Characteristics of global waves based on the third-generation wave model SWAN

    Mar. Struct.

    (2019)
  • I.J. Losada et al.

    Long-term changes in sea-level components in Latin America and the Caribbean

    Global Planet. Change

    (2013)
  • N.Z. Maia et al.

    Analytical model of sea level elevation during a storm: support for coastal flood risk assessment associated with cyclone passage

    Continent. Shelf Res.

    (2016)
  • B. Meyssignac et al.

    Sea level: a review of present-day and recent-past changes and variability

    J. Geodyn.

    (2012)
  • R.D. Montoya et al.

    Exploring changes in Caribbean hurricane-induced wave heights

    Ocean. Eng.

    (2018)
  • R.A. Montoya-Sánchez et al.

    Seasonal and intraseasonal variability of active and quiescent upwelling events in the Guajira system, southern Caribbean Sea

    Continent. Shelf Res.

    (2018)
  • S. Ortega et al.

    Estimation of the wave power resource in the Caribbean Sea in areas with scarce instrumentation. Case study: Isla Fuerte, Colombia

    Renew. Energy

    (2013)
  • A.F. Osorio et al.

    Construction of synthetic ocean wave series along the Colombian Caribbean Coast: a wave climate analysis

    Appl. Ocean Res.

    (2016)
  • M. Alexander et al.

    The influence of ENSO on air-sea interaction in the Atlantic

    Geophys. Res. Lett.

    (2002)
  • C.A. Andrade et al.

    Coastal flooding hazard related to swell events in Cartagena de Indias, Colombia

    J. Coast Res.

    (2013)
  • C.A. Andrade-Amaya

    The Circulation and Variability of the Colombian Basin in the Caribbean Sea (Doctoral Dissertation

    (2000)
  • J.A. Battjes et al.

    Energy loss and set-up due to breaking of random waves

    Coast Eng.

    (1978)
  • D. Barriopedro et al.

    A discussion of the links between solar variability and high storm surge events in Venice

    J. Geophys. Res.

    (2010)
  • G. Bernal et al.

    Patrones de variabilidad de las temperaturas superficiales del mar en la costa Caribe colombiana

    Rev. Acad. Colomb. Cienc

    (2006)
  • N.B. Bernier

    Annual and Seasonal Extreme Sea Levels in the Northwest Atlantic: Hindcasts over the Last 40 Years and Projections for the Next Century (Ph.D. Thesis)

    (2005)
  • J.-R. Bidlot et al.
    (2007)
  • J.-R. Bidlot et al.

    A revised formulation for ocean wave dissipation in CY29R1. ECMWF Technical

    Memorandum

    (2005)
  • N. Booij et al.

    A third generation wave model for coastal regions, Part I, Model description and validation

    J. Geophys. Res.

    (1999)
  • K.F. Bowden

    Physical Oceanography of Coastal Waters

    (1983)
  • L.C. Breaker et al.

    The 154-year record of sea level at San Francisco: extracting the long-term trend, recent changes, and other tidbits

    Clim. Dynam.

    (2011)
  • L.C. Breaker et al.

    Estimating rates of acceleration based on the 157-year record of sea level from San Francisco, California, USA

    J. Coast Res.

    (2013)
  • L. Carrere et al.

    Modeling the barotropic response of the global ocean to atmospheric wind and pressure forcing comparisons with observations

    Geophys. Res. Lett.

    (2003)
  • L. Cavaleri et al.

    Wind wave prediction in shallow water: theory and applications

    J. Geophys. Res.: Oceans

    (1981)
  • D.B. Chelton et al.

    Ocean signals in tide gauge records

    J. Geophys. Res.: Solid Earth

    (1986)
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