Global Fatal Coastal Landslides

Reiss, Malia N; Young, Adam P; Carilli, Jessica

doi:10.5194/egusphere-2026-96

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https://doi.org/10.5194/egusphere-2026-96

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19 Jan 2026

| 19 Jan 2026

Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

Global Fatal Coastal Landslides

Malia N Reiss, Adam P Young, and Jessica Carilli

Abstract. Coastal cliffs shape the world’s coastlines, providing areas of beauty, habitat, scientific discovery, and recreation. However, as erosional features, coastal cliffs can also pose fatal hazards. This paper presents a database of global fatal coastal landslides from public databases and media articles. In total, the coastal landslide database includes 292 fatalities resulting from 114 landslide events from 1927–2024. Landslide events occurred in 32 countries, with the most events in Spain (20), the United States (19), France (14), and the United Kingdom (10), and include two 10-event hot spots on Reunion Island, France, and San Diego County, California, USA. Most fatalities occurred in temperate regions, with about half of events occurring during months with above average precipitation (and half below), differing from databases that include non coastal fatal landslide events driven largely by rainfall. The database is likely incomplete in part from reporting bias, and the analysis presented here should be interpreted with caution. However, the present results suggest that the timing of coastal fatal landslides may be influenced by (a) elevated rainfall causing reduced cliff stability, leading to more failures in wet seasons and (b) time periods of increased tourism and recreational beach activity, exposing more people to coastal cliff failure hazards in relatively dry seasons. The results can help inform beach hazard management.

Received: 07 Jan 2026 – Discussion started: 19 Jan 2026

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Malia N Reiss, Adam P Young, and Jessica Carilli

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RC1:
'Comment on egusphere-2026-96', Anonymous Referee #1, 26 Jan 2026 reply
The paper by Reiss et al. explores a database of 114 fatal coastal cliff collapses over 97 years. Geographic hotspots of coastal landslides were identified, including San Diego County (USA) and Réunion (France). The timing of fatal landslides was further analysed in relation to precipitation in the region during the month of the event. Around 50% of the events occurred during “dry” months, partly because more people were on beaches during these periods. The findings highlight the importance of accounting for both environmental effects on cliff collapse hazards and human behaviour and displacement patterns in coastal hazard management efforts.
The paper is well-written and structured. However, some major points remain to be clarified and further analysed. These are the following general comments:
The title seems misleading, as the paper does not address all coastal landslides but only coastal cliff collapses. Other types of landslides impacting the coast, such as inland slides reaching the coast, debris flows or tsunami impacts from distant slides, were excluded from the database. In addition, the term “global” might be considered overly confident given clear and correctly stated sampling biases.

The distinction between coastal cliff collapses and other landslide events is sometimes difficult. In addition, large landslides impacting the coast or generating fatal tsunamis are excluded from the database (rule number 2, lines 66-68), even though they are coastal landslides and important events for regions such as Alaska and Greenland. A better explanation of why to exclude such landslides and of how to categorise borderline cases is necessary.

While an effort has been made to include more languages than English alone, the database might be skewed towards regions with better records and media access to remote locations, rather than global coverage. It is likely that many events in the global South were not accounted for, particularly single-victim events, events in the typhoon and monsoon seasons, and that additional languages should be included in a global database. An alternative might be to exclude parts of the world where coverage is likely to be too poor.

114 events are a relatively small statistical population. That makes it difficult to draw global conclusions about landslide triggers, particularly because only fatal landslides are considered. Classic statistical tests and measures are not presented for the monthly rainfall analysis. Such tests should account for the link between rainfall and human presence, as these are not independent variables.

The approach of binning each event by monthly total precipitation seems too simplistic. An event at the start of a month would take into account rainfall in the weeks following the event, not in the weeks preceding it. A better approach would be to use the 1-month rainfall total at the time of the event rather than the calendar-month rainfall total. In addition, using the same approach, different rainfall durations (e.g., 3 days or 1 week) could be evaluated, which might be more appropriate for shallow landslides. With this approach, it might be possible to draw more robust conclusions about the effects of intense rainfall events during the dry season. Similarly, at some tourist locations, we could expect more visitors on weekends. A test of landslides grouped by day of the week, during the tourist season and the off-season, might yield new insights for the identified local hotspots.

Additional minor comments are:
Since most events occur in temperate climates and the Northern Hemisphere, the results might not be applicable to tropical, arid, or polar coasts, where landslides may exhibit different temporal dynamics. Once again, this point challenges the “global” approach and should be at least mentioned.

The tools for translating keywords and searching media online were not clearly specified. Were AI tools involved? If yes, which ones?

Thresholds to consider a correlation significant are not discussed.

In addition, I note the following technical corrections:
The lack of an accent for Réunion Island, which should be added.

GDP units seem not correct. The sources for the GDP data are missing. GDP per capita would probably be more appropriate for this analysis.

Figure 1: Black boxes should be removed.

Figure 2: The title should be removed above the figure.

Figure 3: A large number of countries seem to have a single landslide event. This might influence the statistics. Countries with no landslides could be included.

Figure 8 should include a second panel, with the same analysis for Réunion Island.

In the database, the “Victim” category is unclear, the “Tourism season” category is always left empty, and the “Distance from coast” category lacks units and appears binary.

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Citation: https://doi.org/10.5194/egusphere-2026-96-RC1
RC2:
'Comment on egusphere-2026-96', Anonymous Referee #2, 17 Feb 2026 reply
Dear authors,
This manuscript compiles a “Global Fatal Coastal Landslide Database” covering fatal coastal cliff/bluff failures over the period 1927–2024. The authors define inclusion/exclusion criteria for what qualifies as a fatal coastal landslide and aggregate events and fatalities by country. They then explore (i) spatial clustering (“hot spots”), (ii) country-scale statistical relationships between fatal-event counts/fatalities and proxy variables such as tourism, population, GDP, and measures of cliffed coastline, and (iii) seasonality and rainfall context using gridded monthly precipitation. A regional case study (San Diego County) illustrates how visitor seasonality may shape fatality rates. The main interpretive message is that observed fatalities reflect a combination of hazard and exposure, with tourism/population proxies emerging as stronger predictors than simple coastline/cliff metrics, and with rainfall seasonality patterns not providing a clean global trigger signal under the selected precipitation metric.
While the paper presents an interesting topic, several issues need to be addressed. Please find my detailed feedback below, which aims to enhance the clarity and quality of your work.
General Questions:
Is the manuscript primarily intended as a database paper or as a global driver/attribution study? The current conclusions read as causal in places, but the dataset and methods support only limited inference.

How do you separate physical hazard controls from exposure and reporting bias? Given the known under-reporting and strong temporal changes in reporting, how do you justify interpreting country-scale correlations as “drivers” rather than “drivers of reporting and exposure”?

Tourism (single-year) and population (single-year) proxies are used to explain a century-scale record. What sensitivity analyses (e.g., post-1995 subset; decade bins; time-varying covariates) demonstrate that the conclusions are stable?

Event and fatality counts are non-negative, overdispersed, and heavy-tailed. Why not use count models (Poisson/negative binomial), robust methods, or rank correlations? How are leverage points handled without excluding high-consequence events? what uncertainty is reported?

Monthly 1° precipitation cannot resolve short-duration triggers or antecedent hydrology relevant to rockfall and cliff collapse. Do you intend this section as a trigger test or purely as seasonality context? The conclusions should be revised accordingly.

What is the justification for treating high-fatality events as statistical outliers to be removed? In risk research, these events are often of greatest societal importance.

Specific Comments:

Lines 55–63: Please add a concise data dictionary (in-text or supplement) defining each recorded field, allowable values (e.g., trigger categories, victim affiliation), and how “location precision” is quantified.
Lines 80–94: The relationship between the main database and the supplemental spreadsheet needs to be operationally defined (decision rules, intended use, and whether both are released). Please clarify whether “marginally meeting criteria” cases are excluded from all analyses, and how 2025 events in the supplemental file are treated in interpretations.
Lines 95–99: The rule for events with confirmed occurrence but unknown victim count should be stated as an explicit variable/flag in the dataset (so readers can reproduce “event” vs “fatality” analyses without ambiguity).
Also, the citation labeling here appears inconsistent: the South Africa (Durban-area) Landslide Blog entry is cited as Petley (2022a), but the bibliography assigns 2022a to a different event (Philippines). Please correct the a/b label for this citation.
Lines 100–106: The multilingual search procedure is a strength; however, the language list is exceptionally long in the main text. Consider moving the full list to an appendix and retaining only a summarized description (languages, approach, and the role of native speakers) in the Methods.
Lines 115–126: This paragraph is where readers will judge the credibility of the country-scale analysis. Please report, at minimum: (i) the number of countries used in each regression, (ii) exact model specification (response variable, transformations if any), and (iii) influence/diagnostic reporting. The definitions of “hotspot” radius and “fatality outliers” should be justified here (or explicitly motivated with a sensitivity analysis plan).
Lines 127–136: Please explicitly state how missing precipitation matches (events outside the precipitation product window or gaps) are handled downstream, and ensure the rainfall section is written as testing what the method can actually resolve (triggering vs broad seasonality context).
Lines 145–148: The manuscript notes that some sources contain duplicate information. Please add a concise description of the deduplication procedure (matching rules and conflict resolution for fatality counts and dates/locations), and whether the final dataset retains source provenance for merged entries.
Lines 170–181: Please ensure that results reporting includes the sample size (countries) associated with each correlation in addition to r²/p. For “locals vs tourists,” define precisely how affiliation was assigned and how “unknown affiliation” cases are treated in any percentage statements.
Lines 240–250: Please review this discussion for wording that implies causality from the proxy correlations. The interpretation should be written to remain valid under the limitations raised by exposure/reporting bias and temporal proxy mismatch.
Lines 260–266: The in-text citation “Georgopoulou et al. (2018)” appears inconsistent with the bibliography (listed as 2019). Please reconcile the year and ensure the cited reference supports the stated linkage between climate preferences and beach tourism.
Lines 290–295: The in-text “Maerz et al. (2016)” appears inconsistent with the bibliography (listed as 2015). Please correct the year and verify that the cited paper supports the specific claim about manual/mechanical removal of hazardous rock.
Lines 303–305: The in-text “Olsen et al. (2008)” appears inconsistent with the bibliography (listed as 2012). Please correct the year and verify the reference supports the stated lidar-monitoring application.
Also please fix these points:
Correct “Haque et at, 2016” to “Haque et al., 2016” and ensure consistency with the reference entry.

Table 1: “Calvello and Peco raro, 2025” contains a spacing/typo in the author name.

Reference list: Castedo et al. (2017) repeats author strings (appears duplicated).

Fix DOI formatting where “https://doi.org/https://doi.org/…” appears, e.g., Young et al. (2009) and Silva et al. (2016), and Stringham (2015).

Herre and Samborska (2023) reference entry appears to have a missing parenthesis and awkward date punctuation.

Respectfully,

Reply
Citation: https://doi.org/10.5194/egusphere-2026-96-RC2

Malia N Reiss, Adam P Young, and Jessica Carilli

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https://doi.org/10.5194/egusphere-2026-96-supplement

Malia N Reiss, Adam P Young, and Jessica Carilli

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Short summary

Globally, coastal cliffs can pose fatal hazards. We present a global fatal coastal landslide database, with 114 fatal events that killed 292 people from 1927–2024. Using this database, we found that deaths from coastal landslides are partly associated with elevated rainfall, and partly associated with dry periods with elevated human beach activity. These results can help inform beach hazard management.


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