the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Apr 2026
Review article: Rainfall-Induced Landslide Early Warning System: Advances, Gaps, and Perspectives
Abstract. This review is necessary at this time to provide a comprehensive evaluation of the rainfall-induced landslide early warning system (LEWS) through the lens of the United Nations ‘Early Warnings for All’ (EW4All) framework. This study integrates EW4All pillars, incorporates overlooked literature, examines information sharing in academic publications, and evaluates the global feasibility of implementing EW4All for LEWS. Of 61 rainfall-induced LEWS identified in the literature covering 23 countries, 14 are considered operational, meaning they are currently implemented and actively used for warning purposes, across only 10 countries. Among local, regional, and national systems, local LEWS is often less scalable and more resource-intensive. Most operational systems target debris flows and shallow landslides and rely mainly on rainfall thresholds. While some include susceptibility maps, risk maps are largely absent. Real-time sensor data are used in some systems; however, high maintenance costs limit scalability. Reliability is further constrained by data scarcity, limited forecast verification, suboptimal use of AI, and the lack of standardised forecasting approaches. Community engagement and multi-hazard integration remain limited. Although EW4All is transformative, implementing effective LEWS in rainfall-induced landslide-prone areas worldwide by 2027 remains impractical without localised approaches, sufficient funds, and resources.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences.
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.- Preprint
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2026-2156', Anonymous Referee #1, 22 May 2026
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AC1: 'Reply on RC1', Roquia Salam, 08 Jun 2026
Dear Reviewer,
Thank you very much for your careful reading of our manuscript and for your constructive comments. We have addressed the comments in detail in the attached supplementary response file. The manuscript has been revised accordingly, with particular attention to clarifying the EW4All-based contribution, strengthening the analytical interpretation of the results, expanding the discussion of operationalisation, governance, scalability, susceptibility mapping, AI, Pillars 3 and 4, and revising the Conclusion to better highlight the main scientific messages and future priorities.
We are grateful for your helpful feedback, which has improved the manuscript substantially.
Sincerely,
Roquia Salam, on behalf of all co-authors.
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AC1: 'Reply on RC1', Roquia Salam, 08 Jun 2026
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RC2: 'Comment on egusphere-2026-2156', Anonymous Referee #2, 10 Jun 2026
General comment
This review article evaluates rainfall-induced landslide early warning systems (LEWS) in the lens of the UN's Early Warnings for All (EW4All) framework. The authors reviewed 61 publications covering 23 countries looking at key gaps across the four EW4All pillars. The topic is relevant to NHESS. However, the manuscript in its current form has significant methodological, structural, and presentational weaknesses that must be addressed before it can be reconsidered for publication.
Specific comments
On the paper selection and analysis
- My main concern regards the inclusions of forecasting systems in the analysis, which could be misleading and led to biased findings. Looking at Table 1, the vast majority of the 61 reviewed studies address only EW4All Pillars 1 and 2 (risk knowledge and detection/monitoring/forecasting). Very few address Pillars 3 and 4. This pattern is not incidental, indeed it directly reflects the inclusion of forecasting-only studies that, by the authors' own definition, are not EWS at all. As a matter of fact, the headline finding "14 of 61 LEWS are operational" is potentially misleading because it is unclear how many of the 61 are proper early warning systems versus forecasting tools that were included despite not meeting the paper's own definition of EWS. Moreover, the claim to evaluate LEWS "through the EW4All framework" is undermined because including forecasting-only systems guarantees that Pillars 3 and 4 will appear absent, not because they are genuinely missing from practice, but because the included studies were never designed to address them. Thus, I suggest to discard the forecasting systems from the review and focus only on proper LEWS, considering if necessary all LEWS already mentioned in the previous review articles (cited in the text). I think that including all LEWS in the review will allow a more detailed evaluation of all EW4All Pillars. As an example, Hong Kong, Rio de Janeiro, Japan LEWS are left out from the main discussion, despite being consolidated systems with rich peer-reviewed and grey literature. I would suggest having a look also on the LEWS catalogue produced by LandAware, the international network on LEWS (https://www.landaware.org/glossary-and-catalog-of-lews/).
- The 61 selected articles appear to include a wide range of publication types, from peer-reviewed empirical studies to conference proceedings and grey literature. The authors do not apply any quality assessment criteria to the included studies. Without this, high-quality national operational systems receive equal weight to conference papers describing prototype models.
Seven grey literature sources are included, but the selection criteria for these are vague. How were national websites identified? Was the search systematic or opportunistic? This needs clarification.- Moreover, some papers are included in 61 selected papers (Table 1) even though they do not describe early warning systems or forecasting systems. As an example, the paper by Guzzetti et al. (2024) describes a validation of a deep-learning model for landslide prediction; moreover, the three papers from Zhang et al. (2024) – which are wrongly cited as 2024, 2024a and 2024b – describe criteria for analyzing landslide displacements with the aim of early warning, but they do not describe a forecasting system; the article by Thomas et al. (2023) describes the application of a slope stability model in some locations, but is far from the definition of a comprehensive forecasting model.
- The paper does not describe a structured data extraction protocol or inter-rater reliability process. Given that the review has a single primary analyst (RS), there is a risk of extraction bias, particularly for the EW4All pillar assignments in Table 1. Several pillar assignments appear to be inferred rather than explicitly reported in the source articles (as acknowledged in Section 3). This needs to be flagged more prominently as a limitation, and the basis for each assignment should be made transparent, perhaps via supplementary material.
- The paper does not dedicate any structured analytical section specifically to local-scale LEWS, despite them constituting nearly a third of the reviewed systems (19 of 61). Indeed, a previous review on local-scale LEWS (https://link.springer.com/article/10.1007/s10346-018-1068-z) is not mentioned. The gap analysis in Section 4.5 discusses challenges generically across all LEWS types without disaggregating by scale. There is no comparative discussion of how the challenges and EW4All pillar coverage differ between local, regional, and national systems — even though Table 1 encodes this information. The community-based LEWS literature is particularly relevant to EW4All's people-centred mandate but receives no dedicated treatment.
- The novelty claimed in Section 1.4 is overstated. The authors assert that the distinct contribution lies in "reframing the field through the EW4All framework," yet the analytical application of the framework remains largely descriptive rather than evaluative. The paper identifies well-known gaps (data scarcity, lack of risk maps, limited community engagement) that have been extensively documented in prior literature. The authors do not demonstrate how the EW4All lens generates new insights beyond organising pre-existing knowledge under new headings. The theoretical contribution would be strengthened considerably if the authors more explicitly articulated what the EW4All framing reveals that prior frameworks did not. Section 1.4 should be improved to make a more defensible claim about novelty.
On data analysis
- The core analytical tool is a series of radial diagrams (Figures 7, 8, 9) that have serious readability problems. The analysis they represent is largely descriptive and categorical. The paper does not attempt any cross-tabulation, correlation, or comparative analysis between LEWS characteristics and operational status, geographic region, or EW4All pillar coverage. For example: Are LEWS that use susceptibility maps more likely to be operational? Do national-scale systems address more EW4All pillars than local ones? These questions are implied but never tested.
- The distinction between operational and non-operational systems is central to the paper's argument but is applied inconsistently. The authors note (Section 3) that some LEWS in the "validated" stage are recommended for implementation, and that operational status does not necessarily mean full areal coverage. These nuances are important but are not systematically incorporated into the analysis.
- Section 4.3 is, in my opinion, the weakest part of the paper. The authors essentially acknowledge that the literature review cannot address Pillars 3 and 4 because the academic publications do not cover them adequately. While this is an honest and valid observation, a paper claiming to evaluate LEWS "through the lens of the EW4All framework" cannot do so when two of the four pillars are almost entirely absent from the analysis. The authors should either supplement this section with a targeted review of grey literature, agency reports, and policy documents on warning dissemination and community preparedness, or clearly reframe the paper's scope in the title, abstract, and objectives to reflect that the analysis is primarily limited to Pillars 1 and 2.
- The feasibility analysis in Section 4.6 is conceptually interesting but reads as opinion rather than evidence-based analysis. Several of the six feasibility concerns are presented without citation or empirical grounding. For instance, the claim that "ensuring complete inclusivity is practically impossible" is asserted without reference to any case studies or theoretical literature on emergency management and inclusivity. Similarly, the critique that the EW4All framework "emphasises the application of similar methods or models everywhere" is a characterisation that the authors do not substantiate with specific textual evidence from the EW4All documentation. These claims may be correct, but they must be supported.
On the presentation quality
- The radial diagrams (Figures 7, 8, 9) represent a major presentational problem. Each diagram attempts to display three or four variables simultaneously for 61 LEWS using country codes as labels around a crowded circle. The result is essentially not readable without extreme magnification. Individual labels frequently overlap, and the legend abbreviations require readers to cross-reference constantly. These figures, in their current form, do not meet minimum readability standards for a journal article. The authors should replace or substantially redesign these figures. Heat maps, grouped bar charts, or structured tables would convey the same information far more clearly. If the authors wish to retain the radial format as a visual overview, they should provide supplementary tables containing the underlying data in accessible form.
- Table 1 is comprehensive and generally well-constructed, though it is very long and would benefit from a brief inline summary noting the distribution of studies by continent and EW4All pillar coverage. Table 2 is useful for contextualising the additional LEWS from prior reviews.
- The English writing is generally good. Given the authors' acknowledgement that AI tools were used for some paragraphs, the editors should be aware that several passages have a smoothed but imprecise quality that may have been introduced through AI assistance rather than corrected.
- The paper's structure is broadly logical, but the relationship between Sections 4.4 and 4.5 is confusing. Section 4.4 discusses "additional LEWS from review papers" using a different data source than the main 61 articles, while Section 4.5 analyses gaps in the 61 articles. The boundary between these sections is unclear, and the discussion of the additional 30 LEWS in Section 4.4 partially duplicates findings already made about the primary 61 in Sections 4.1–4.2. The authors should clarify how the findings from the two datasets are analytically distinguished and whether the additional 30 LEWS inform the gap analysis in Section 4.5.
- I'd suggest to specify in the title that the review and analysis were done in the lens of EW4All framework.
Other minor comments
- Section 1.3: The claim that "88% of landslides are rainfall-triggered" (citing Haque et al., 2019 and Froude & Petley, 2018) should be contextualised. This statistic relates to fatal landslides in a specific database and time period, not to landslides in general. The generalisation as stated is imprecise.
- Section 4.5.2.5: The claim that "there is no known standard methodology for developing LEWS" is an important finding that deserves more discussion. The authors could usefully reference the work of Calvello (2017) and Guzzetti et al. (2020) on performance evaluation frameworks, which represent partial steps toward standardisation.
- Section 4.6: The six feasibility concerns should be structured and numbered consistently with the gap analysis in Section 4.5 for readability. Each concern should be supported by at least one citation or concrete example.
Citation: https://doi.org/10.5194/egusphere-2026-2156-RC2
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This manuscript deals with an important topic, namely rainfall-induced landslide early warning systems (LEWS) framed within the United Nations “Early Warnings for All” (EW4All) initiative. The subject is highly appropriate for NHESS, as it combines physical hazard forecasting with operational, institutional, and governance aspects related to risk reduction that are often neglected. The international perspective adopted is valuable, as it allows the reader to appreciate the heterogeneity of LEWSs implementation. The paper reflects the effort in reviewing a lot of literature. A clear added value is the attempt to interpret LEWS literature through the EW4All framework, which provides a wide perspective compared to more conventional reviews focused mainly on rainfall thresholds or modeling approaches. The manuscript brings attention to several important operational issues, including data limitations, the warning dissemination, and the role of community involvement. At the same time, a recurring aspect is the repeated emphasis on a limited number of concepts, EW4All, the scarcity of operational systems, data constraints, and the use of artificial intelligence. These are central themes, but they tend to reappear too frequently across sections, sometimes without adding new insight. A clearer structuring of these ideas, with less repetition and more cross-referencing, would make the argument better. I recommend a major revision before that the manuscript can be reconsidered for publication. My comments are provided in the attached file.