Rocky coast erosion (i.e., cliff retreat) is caused by a complex
interaction of various forcings that can be marine, subaerial or due to
rock mass properties. From
Rocky coasts are characterized by dynamically linked cliff retreat and shore
platform erosion
Evidence of the sea driving coastal cliff erosion. The vertical shaped cliff in the foreground is similar to the cliff in the background (its smoothed shape), except that the one in the background has been protected from the sea by a sand spit. Obviously, the cliff with sea at its base then retreats more quickly (the cliff face is more or less vertical). Photo from Punta Quilla, Patagonia, Argentina.
These drivers can be divided in three groups, depending on their nature
(Fig.
Each of these have been proven to be efficient in their own way in cliff
retreat phenomena, but their relative importance is perceived differently
across studies (Fig.
Some studies aim at quantifying cliff retreat rates at the regional scale,
i.e., coastal sections of several tens to hundreds of kilometers. These
studies often pertain to risk management
In order to overcome biases inherent to individual approaches, studies have been conducted at
global scale. They are often based on morphometry; for
example, the classic study by
Since
We updated the dataset from Sunamura (1992) into the new GlobR2C2 (Global Recession Rates of Coastal Cliffs) database by taking advantage of all the existing site and regional studies, and built a worldwide cliff recession database. This database is used in a new approach to link documented erosion rates and external forcings. It also allows researchers to look at the relative efficiency of forcings in relation to one another, in order to explain erosion rate variations at the global scale. The benefits of this global approach are that it erases local specificity and seeks to define global trends. The links between cliff retreat and environmental parameters were explored statistically. However, the synthetic database approach is limited in that it compiles the information available for all studies at once. In that sense, it reduces information to the largest common denominator. Therefore, the main goals of this paper are as follows: (i) to compile a review of online literature in English, French or Spanish from peer-reviewed publications or national databases providing cliff retreat rates; and (ii) to link a dependent variable (erosion rate) to independent variables (cliff and meteo-marine settings). This analysis demonstrates the predominance of factors leading to cliff retreat. The GlobR2C2 data are available in the Supplement.
The main goal of this study is to link cliff retreat rate to external forcings at global scale. Those data exist in peer-reviewed journal articles and national databases. Peer-reviewed articles were chosen as the source of cliff descriptions and erosion rate values and settings. However, marine and continental forcings conditions are often reported in a very heterogeneous fashion. This information can either be completely lacking, incomplete or described in inconsistent ways. To overcome this issue, external global databases were used to harmonize forcings (i.e., tidal range, swell height, rainfall and so on; see Sect. 2.3.6 to 2.3.9). They provide standardized and reputable information for cliff height, sea condition and atmospheric climate.
The different steps of the study described in subsequent paragraphs are as follows: (i) the design and filling of a relational database with raw data, (ii) post-processing on database fields in order to tidy up the data and (iii) statistical exploration of links between erosion and forcings.
To organize the disparate knowledge reported in the literature, a rigorous
analytical framework is an absolute necessity upstream of any data capture.
We opted for a relational data base framework where the architecture was
designed according to the Merise method
Conceptual data model of the GlobR2C2 cliff erosion database. Primary keys are underlined and numbers are cardinalities.
Here, GlobR2C2 was structured with two objectives in mind: (i) compiling
original information and faithfully tracing publication sources, and
(ii) anticipating analytic queries of the database designed to answer
geomorphological questions. The database is structured to keep track of
information relative to publications, sites, measurements and contextual
information of the cliffs, or their environment. Specific care was taken to
separate original data from information derived by us, and to distinguish
between article information from auxiliary datasets (Fig.
The final conceptual data model contains 11 entities and 76 attributes. A
conceptual model is given in Fig.
GlobR2C2 (Global Recession Rates of Coastal Cliffs) database v1.0 was populated with data from two main types of published sources: published peer-reviewed English journal articles, and official but non-peer-reviewed studies arising from official organizations (e.g., the CEREMA French risk survey) in English, French or Spanish. Journal articles were selected when they reported quantified values of cliff recession rates and described the quantification method. The search was initiated with bibliographic web search engines (Web of Science, Google Scholar) and expanded using citations therein. We recognize that some references may have escaped our attention. We are keen to expand the database further with the contribution of the community. The version presented in this article is version 1.0. compiling references up to 2016.
The “cliff” and “lithology” entities contain information related to cliff morphology (i.e., height, length) and rock property (i.e., lithology, fracturing, weathering, folding, bedding).
Cliff geology may exhibit a very complex set of lithologic types, contact relationships, inherited tectonic structures and overprinted weathering. Authors often do not systematically report on these characteristics. Confronted with the heterogeneity of parameter presentation, we synthesized information in the following manner. A lithological name fills the “lithology” entity and a position field records rock position along the cliff (numbered from cliff toe to cliff top). Additional descriptions were copy/pasted in comment fields in order to preserve the original description. By comparison, rock state (weathering, folding, faulting, bedding etc.), is rarely mentioned. This could be because the cliffs do not present any such characteristics, or because authors did not think it was relevant and did not mention it. Moreover, parameters describing rock state are either complex, technically expensive to describe and quantify, or outside the authors' scientific field of expertise. They were characterized with a Boolean value (True/False) to be integrated in the database. “True” refers to the presence of fracturing/weathering mentioned in the paper. “False” means that authors either describe fracturing/weathering as non existent/negligible or it is not mentioned in the paper.
Cliff location is entered as geographic coordinates. Studied cliff site extent was digitized from publication information and mapped using Google Earth. A primary key links this geographic file to the database.
The measure entity contains the erosion rate values and measurement methodology (how erosion was measured, for how long, with what detection threshold). Erosion is generally provided as an erosion rate in meters per year, occasionally as finite retreat (in meters) or as minimum and maximum erosion rates or eroded volume (in cubic meters).
Cliff retreat measurement errors and time spans were also recorded. Measuring
sea cliff erosion presents a wide range of techniques. Those techniques vary
significantly in terms of the following: (i) accuracy, which range from field observation and
“expert” estimates
One-dimensional cliff retreat measurement techniques correspond to retreats calculated on
single transects. Typically, they correspond to measurements made with peg
transects that record the cliff toe retreat or transects on aerial photographs
to quantify cliff-top retreat
The French CEREMA institute published a systematic national coastal cliff
recession inventory
Based on historical aerial photograph archives, CEREMA acknowledges that the
quality of photographs limits the detectable cliff recession to rates higher
than 10 cm yr
The tidal range describes the variation in the height of the water surface.
One consequence is that the cliff and platform undergo cyclic wetting and
drying that weakens and erodes the constituting rocks
Field estimates of uniaxial compressive strength
Wave properties were extracted from the ERA-interim reanalysis dataset
Anticipating that mean sea state values may be deceptive metrics, a record of
extreme events was also described. Those events were characterized by the
95th percentile of wave significant height as suggested by
Climatic information was extracted from Climate Research Unit data between
1961 and 1990
Cliff height often appeared to be missing. Filling this value is not
straightforward because cliff height can be strongly variable along the
surveyed cliff. Nevertheless, in order to provide a robust
estimate, a mean cliff height was extracted from the 7.5 arcsec
spatial-resolution GMTED2010 data global DEM
The first purpose of the database is to collate raw data from original sources in the most traceable manner possible. This data does not necessarily report information in an easily accessible fashion. This may be because (i) fields translate different realities (e.g., recession rates vs. retreat values or recession rates relate to profile-specific recession rate or to kilometer long cliff sections), or (ii) value instances of a field are too broad and need summarizing in fewer categories (e.g., lithology). Thus, post-processing was applied to the database in order to make it more homogeneous and more readily usable for statistical analysis.
We mentioned earlier that measurement techniques were either 1-D, 2-D or 3-D. These methods do not reflect the exact same processes and a choice was made to force all measurements to homogeneously report 2-D type measurements. The 3-D measurements in cubic meters per year were divided by cliff face surface in a cliff top equivalent retreat in meters per year. One-dimensional measurements do not average information laterally. Cliff retreat is stochastic in time and space and 1-D measurements profiles may happen to quantify erosion on a particulary high or low erosion transect. Therefore, erosion rates of the transect measurements were averaged for a unique study, cliff and period of time in order to limit the risk of over- or under-representation.
Original data may be provided in different ways (for example the time span between two measurements may be given by a duration or by start and end dates). As often as possible this information is summarized in a single duration field with a homogeneous unit. The following are the operations performed:
To obtain a duration in years, the fields measure duration (year), measure beginning and measure ending (date) were merged together. Retreat (m) and eroded volume ( The mean cliff height was either obtained from a cliff height mean field or as the mean between height min, height max (m). The error (m yr
Some explanatory variables were strongly correlated with each other (e.g., wave period vs. wave significant height). This redundant information may lead to spurious correlation. Therefore, new synthetic variables combine existing variables:
Cliff site locations (red dots) and number of studies contained in the GlobR2C2 database by country (published before 2016).
Monthly mean temperatures were converted to mean annual temperature and amplitude. Deep water swell energy flux was computed using swell period and significant
height
Swell incidence angle with respect to the cliff (angle between 0 and 90
Time line of cliff erosion publications recorded in GlobR2C2 differentiated by measurement method.
The database, filled with information from publications, results in more than
40 distinct lithological descriptions. We first grouped lithology into 9
groups with a similar classification to that of
Cliff recession rates differentiated using the Hoek and Brown rock mass strength criterion, which merges lithological descriptions and the fracturing/weathering state of the cliff rock.
Aggregation criteria are based on the fields lithology name, weathering,
fracturing and comments, in which all published details on rock strength,
structural geology, weathering were preserved. Rocks were classed into three
resistance classes termed hard, medium and weak. One may note that a similar
approach, but with only two classes, was adopted by the EUROSION project
consortium
Erosion rate versus marine forcings (wave energy flux (W), tidal
range (m) and number of storms) for each one of the Hoek and Brown rock
resistance class. Lines beneath the scatterplots represent moving median per bin
and the numbers are the Spearman correlation coefficients, which were only
reported when the
The database is filled with 58 studies, which is comprised of 47 peer-reviewed articles and 11 public national databases, documenting 1530 cliff sites and 1680 erosion rate records. Indeed, some cliff sites were repeatedly measured over different periods. With more than 90 % of fields complete, the database is satisfactorily thorough; however, the constitution of the database highlights some characteristics that are often poorly reported. We previously mentioned the difficulty regarding finding a description of cliff rock weathering and fracturing. Those fields are missing for 98.4 % of the records (corresponding to 53 publications).
Studies are mostly concentrated in Europe (42 studies, 1579 records), in
Oceania (focused mainly on New Zealand) (3 studies, 94 records) and Northern
America (4 studies, 50 records). Asia (2 studies, 4 records) and South
America (1 study, 1 record) are poorly represented. No literature was found
for the entire African continent. This lack is confirmed by the absence of a
chapter about Africa in
The number of studies has steadily been growing since the mid-1990s
(Fig.
Spearman rank correlation matrix between forcing and erosion rate
for the three types of rock resistance. Values in black are Spearman
correlation coefficients. Grey values are the associated
Erosion rate versus climate forcings (frost day frequency (days),
annual cumulated rainfall and (mm) mean annual temperature (
Reported studies describe coastal processes along 20 m to 6.4 km stretches of coastline. The median length is 600 m. Total survey durations vary from just 1 month to 7100 years, although half the data lie between 56 and 63 years given the bulk of aerial photograph comparison studies.
The purpose of the database is to examine the relationships between erosion rates, site conditions and external forcing. Those links were sought by means of statistical exploration data analysis (known as EDA).
One of the first influential factors often pointed to in literature is rock
resistance
Ranges of erosion rates within different lithology. Comparison between the study by Woodroffe 2002 and this study.
Macroscopic rock mass strength classes, although possibly crude, exhibit the ordered behavior expected from the literature: weak rocks erode faster than medium strength rocks, and medium strength rocks erode faster than hard rocks. Central erosion rate values increase by a factor of 2 to 3 from one class to the next.
These values are in agreement with Woodroffe's work
(
In order to explore the influence of sea aggression, several variables were
implemented in the database describing mean sea agitation and tidal range,
and sea agitation during extreme events. All the variables concerning swell
are strongly correlated. Hence, only three independent marine parameters are
analyzed in the scatterplots in Fig.
All scatterplots appear to be widely spread and do not show simple linear
relations. Indeed, the Spearman rank correlation coefficients, which evaluate
monotonic relations between two variables, are low (Fig.
Concerning climatic forcings, recession rates are compared to temperature
variation, frost frequency and the amount of rainfall. As for marine forcings,
data is very scattered (Fig.
The GlobR2C2 database provides a quantitative overview of the current coastal
rocky cliff erosion knowledge. This database is the first update since
The GlobR2C2 database is based on bibliographic references as well as models and reanalysis, which are used as proxies for forcings; some biases are inherent to this kind of approach. The next paragraphs focus on different aspects of these limitations due to (i) the use of cliff retreat rate as a proxy of erosion, (ii) the use of models and reanalyses as proxies of forcing and (iii) the use of peer-reviewed journals.
Statistical exploratory data analysis is a way to dissolve local
particularity into a global analysis. Nonetheless, including every
quantitative study implies mixing rates measured via different methods,
accuracy, and spatial and temporal extents, which could be a source of bias.
Erosion is stochastic: the occurrence of a big rare event would influence the
actual figure of the observed retreat rate.
GlobR2C2 addresses the concern of non-representative erosion values
by compiling all studies available online, and retaining information from all
sites and survey periods. Therefore, the actual dispersion of recession
rate values is preserved, which allows for the recognition outlying values
(Fig.
While publication-derived cliff recession rates and cliff conditions could be forced into a coherent database framework, environmental forcings were so scarcely and heterogeneously documented that the same rationalization process was not possible on the basis of publication alone. Instead, publicly available global climatic and sea condition databases were used. These databases present the advantage of being spatially and temporally continuous thanks to reanalyzed climate and sea state models. Their principal limitation is their coarse-grained definition compared to site specificities. Nevertheless, they document external forcings (i) in a uniform fashion (regular spatial and temporal sampling steps), (ii) for the entire globe and (iii) reflect forcing condition for durations spanning several decades. Consequently, even if regional or continental datasets offer higher resolution information in space or time, the global extent ensures that all cliff sites worldwide are uniformly documented.
GlobR2C2's worldwide compilation shows that research in this domain is very active. A large body of quantitative data already exist. However, even if data coverage is somewhat global, publications have been found to focus primarily on a few western countries. This finding also reflects the strategy of literature search adopted: only international and national literature published in English, French or Spanish were compiled. Due to the language barrier, we are aware that studies in Russian, German or Japanese, among other languages, were unwillingly omitted.
Spatially, our search strategy did not flag scientific literature on the evolution of African and South American cliffs. Cliff recession studies appears to be focused on the richest areas where economically valuable coastal assets are exposed to losses. This geographic distribution induces an overrepresentation of temperate climates and a limited presence of some extreme climates or wave conditions like equatorial or polar regions. These underrepresented extrema could be the key to understanding the effects of climate and wave conditions on cliff erosion.
Survey time vs. erosion rate by groups of measurement techniques.
Furthermore, studies focus on fast eroding coasts because they represent bigger risks
and also due to of methodological limitation. Indeed, the French CEREMA
study provides the majority of the erosion values for hard rocks (265 values
from 343, 77 %) and medium rocks (47 values from 66, 71 %). Without this
systematic study soft rock represents 75 % of measured cliff retreat. This
fact biased the analysis by mostly documenting erosion distribution in higher
values. The weight of this bias can be appreciated thanks to the French
CEREMA study. This study contains null erosion values for coastal sectors
where the cliff was not seen to recess in a detectable manner on historical
photographs. However, this detection threshold is deemed to be of the order of
10
The cliff retreat rates discussed here cannot capture the overall rock coast
erosion complexity. In particular, it is obvious that the rock shore platform
coevolves with the cliff
On the one hand, platform width may be a powerful proxy for long-term cliff
retreat. However, this analysis is not currently possible due to the fact the seaward
platform boundary is not obvious
Beyond its width, the rock platform behavior encompasses the dynamics of the
scree apron lying on it and possibly shielding it from sea action
Distribution characteristics of cliff erosion rates (in
m yr
This bibliographic synthesis has highlighted the strengths and weaknesses of the current rocky coast research efforts. The trend over the last three decades has gone towards increasing the quality and the resolution of cliff recession data and documenting a growing number of sites, which is positive. However, what this study highlights is the lack of a description of critically useful parameters to aid in understanding cliff evolution dynamics, which includes the following: (i) cliff height; (ii) finer rock mass characteristics descriptions, in particular weakening phenomena such as weathering and fracturing; and (iii) foreshore descriptions, in particular the type (sand beach/pebble beach/rock platform) and geometry (elevation, slope, width) of the foreshore. Moreover, the geographical distribution of the sites studied highlights a major gap in knowledge regarding extreme climates (tropical, equatorial and glacial), slowly retreating cliffs and medium resistance rock types. We also found that literature concerned with cliff retreat was not simultaneously trying to link shore platform processes to cliff retreat or to how local variations specifically affected cliff retreat.
Compared to continental cliffs, coastal cliffs obviously erode
more quickly due to the presence of the sea. The GlobR2C2 v1.0 database compiles ca.
2000 coastal rocky cliff retreat data from an online global literature search
published before 2016. It is the first attempt of its kind since Sunamura's
seminal publication in 1992. The investigated period adds information arising
from the quantitative revolution of lidar technology and the use of the structure from motion
(SfM) technique, which is accessible to scientists with little background in
photogrammetry, in addition to the massive release of aerial photographic archives from mapping
agencies in western countries. The data compiled in GlobR2C2 is
heterogeneously distributed in terms of retreat rates, geographical location,
cliff nature and climate settings. Even if further research should aim at
completing little studied geomorphic contexts of the globe, existing
information clearly shows that cliff retreat is most clearly governed by the
lithological nature of the cliffs. The dependence of cliff recession rates on
rock types is best expressed using a geotechnical parameter, the
Together with cliff settings compiled from publications, GlobR2C2 also records continental climate and marine conditions at study sites from reanalyzed models for their global, spatial and temporal sampling regularity. Both forcings exhibit a weak relationship with cliff recession rates. However, in relative terms, climate (i.e., frost days frequency) exhibits a stronger influence than marine forcing. The influence of the sea is only slightly visible in this dataset through the maximum efficiency of erosion for tidal ranges between 1 and 3 m.
Our data divides rocky coasts into three classes of resistance, following the Hoek and
Brown parameter. The most resistant (least resistant) rocks are
found to lead to retreat rates of less than 10
We conclude at this stage that coastal rocky cliff erosion is primarily
driven by cliff settings with second-order but non-negligible modulations
from marine and continental forcings (Fig.
The GlobR2C2 data are available in the Supplement.
The supplement related to this article is available online at:
MP built the database with the help of YA. Filling of the database and its analysis were performed by MP with help and advice from VR and TD. MP wrote the paper with contributions from the other authors.
The authors declare that they have no conflict of interest.
Mélody Prémaillon's PhD fellowship was funded in equal part by BRGM (the French geological survey) and the French region Midi-Pyrénées. Laurent Roblou and Dominique Astruc are warmly thanked for advice about tide calculation and wave power, respectively. We thank Sebastien Carretier, Christophe Garnier, Elise Nardin and Delphine Rouby for their support and wise advice during committee meetings.Edited by: Mary Bourke Reviewed by: Larissa Naylor and Cherith Moses