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Landslide Early Warning Systems: Resources or Problems?
Fausto Guzzetti1,2
1
2

Dipartimento della Protezione Civile, via Vitorchiano 2, 00189 Roma, Italy
Consiglio Nazionale delle Ricerche, via della Madonna Alta 126, 06128 Perugia, Italy
Abstract Recent estimates suggest that landslides occur in about 17.1% of the landmasses, that about 8.2%of the global population live in landslide prone areas, and that population exposure to landslides is expected
to increase. It is threfore not surprising that landslide early warning is gaining attention in the scientific andthe technical literature, and among decision makers. Thanks to important scientific and technologicaladvancements, landslide prediction and early warning are now possible, and landslide early warning systems(LEWSs) are becoming valuable resources for risk mitigation. A review of geographical LEWSs examined
26 regional, national and global systems in the 44.5-year period from January 1977 to June 2019. The study
relevaled that only five nations, 13 regions, and four metropolitan areas benefited from operational LEWSs,
and that large areas where landslide risk to the population is high lack LEWS coverage. The review also
revealed that the rate of LEWSs deployment has increased in the recent years, but remains low, and thatreniewed efforts are needed to accelerate the deployment of LEWSs. Building on the review,recommendations for the further development and improvement of geographical LEWSs are proposed. The
recommendations cover six areas, including design, deployment, and operation of LEWS; collection andanalysis of landslide and rainfall data used to design, operate, and validate LEWSs; landslide forecast models
and advisories used in LEWSs; LEWSs evaluation and performance assessment; operation andmanagement; and communication and dissemination. LEWSs are complex and multi-faceted systems that
require care in their design, implementation and operation. To avoid failures that can lead to loss ofcredibility and liability consequences, it is critical that the community of scientists and professionals who
design, implement and operate LEWSs takes all necessary precautions, guided by rigorous scientificpractices.

1 Introduction

2 Terminology

Caused by meteorological and geophysical triggers –mostly precipitation and earthquakes [1] – and by avarierty of human actions, landslides of all types,
including debris flows [2], sculpt the slopes of all
mountain ranges, carrying sediments to rivers, lakes, andthe seas. Recent estimates indicate that landslides occur inabout 17.1% of the landmasses, that about 8.2% of the
global population live in areas prone to landslides [3], andthat population exposure to landslides is high [4]. Where
they occur, landslide pose a severe threat to people, often
claiming lives [5, 6]. Given these figures, it is not
surprising that landslide early warning is gaining attention
in the scientific and the technical literature [7, 8], andamong government officials, decision makers, and the
public.
In the talk, I shall discuss the main characteristics andlimitations of early warning systems (EWSs), andspecifically of landslide early warning systenns (LEWSs).
This extended abstract, and the presentaion, are organized
as follows. After an introduction on the terminology used
for EWSs for various natural hazards, including
landslides (section 2), I present the general assumptions
for landslide early warning (section 3). Next, I summarize
the results of a systematic review of 26, regional, national,
and global LEWSs from 1977 to 2019, globally (section
4). Then, I present recommendations for the further
development and improvement of geographical LEWSs,
and to increase their reliability and credibility (section 5).
I conclude (section 6) with remarks on the value andissues of LEWSs.

There is no general agreement in the literature on thelanguage to be used to describe early warning systems fornatural hazards, including LEWSs. According to the
Oxford Learner’s English Dictionary, an “early-warning”
is a “thing that tells you in advance that something serious
or dangerous is going to happen”, and an “early warning
system” is “a condition, system, or series of procedures
indicating a potential development or impending
problem”. With my co-authors [8], we defined an early
warning system as “a device, system or set of capacities
that generates and disseminates timely and meaningful
information to enable individuals, communities, andorganizations threatened by a hazard to act timely andappropriately to avoid or to reduce the impact of thethreat” [8]. In the literature, when discussing LEWSs, theconnotation of “early” depends on the type of hazard, andthe perspective and responsibilities of the individuals andorganizatios issuing, receiving, and using the “warning”,
which is an advice, recommendation, or order to take anaction.

3 Early warning systems
Early warning systems are “non-structural” (“soft”)
measures designed, implemented, and operated to avoid
or to minimize the impact posed by hazards [9]. Examples
of EWSs exist for different natural hazards [9, 10]
including e.g., floods [11], volcanic eruptions [12],tsunamis [13], and snow avalanches [14], and are being
investigate for earthquakes [15]. Landslide early warning
systems are EWSs dedicated to landslides [7, 8].

© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

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3.1 Rationale for landslide early warning
Buildig upon ideas introduced originally by R.H. Cambell
in 1975 [16], and firstly applied in Hong Kong [17] andin the San Francisco Bay Area, California (USA) [18, 19],
the rationale for landslide early warning, and for thedesign, implementaion, and operation of LEWSs, relays
on nine main assumptions [8].
The first two assumptions are that landslides can bepredicted, in space and time, and that “the past is the key
to the future” i.e., the Uniformitarianism principle [20,21], and hence that data and information on past landslide
events can be used to construct landslide predictivemodels. Albeit there is nothing in the literature thatprevents landslide prediction, these assumptions have notbeen demonstrated (or disproved), theoretically [22, 23].
Still, it is important to clarify the meaning of the term
“prediction” [22], that the prediction must be
scientifically based [24], and that one understands andaccepts the limits of a prediction [25].
The next two assumptions are that rainfall is theprimary trigger of (rainfall-induced) landslides, causing
their initiation through infiltration of water into the slope;and that rainfall is a good proxy for the groundwaterconditions that lead to slope instability [16, 19, 26, 27].
Further assumptions are that a rainfall or hydrologicalthreshold is a reliable descriptor of the behaviour of aslope forced by rainfall [19, 26-28]; and that rainfall canbe measured and forecasted with the spatial and temporal
accuracy necessary to predict landslides [19, 29]. Theseassumptions are a clear simplification of the complex
processes that cause a slope forced by precipitsion to fail.
The last three assumptions have to do with the efficacy
of LEWSs, and postulate that landslide forecasts can beused to issue useful landslide advisories; and that basedon a landslide advisory the population can take actions tominimize landslide risk [19]; and that there can be
sufficient time to warn people leaving in potentially
dangerous areas [19, 30]. These later assumptions appear
reasonable, but also have not been proven [8].

existing) landslide, or a debris flow in a single monitored
channel; or (ii) to precict and warn about the possible
occurrence of populations of landslides in a large or verylarge area i.e., many landslides caused by a single trigger
or a number of triggers in a relatively short period [22,23]. The two groups work typically at very different
spatial – and to some extent temporal – scales. The lattergroup encompasses LEWSs referred to as “territorial” [7]or “geographical” [8]. In the following (and in the talk), I
concentrate on geographical LEWSs.

4 LEWS review
A recent review of geographical LEWSs [8] examined
critically 26 regional (19), national (6), and global (1)LEWSs in the 44.5-year period from January 1977 to June
2019. The systematic study relevaled that, at the date of
the ananlysis, only five nations, 13 regions, and four
metropolitan areas benefited from operational LEWSs,
and that large areas where fatal landslides are frequent andlandslide risk to the population is high, lack LEWS
coverage. The evidence is a problem for landslide risk
mitigation. The review further revealed that the rate of
LEWSs deployment has increased in the recent years, but
remains slow.
Examination of the 26 LEWSs revealed that most
systems have undergone some sort of verification, butalso that no accepted standard exists to evaluate theperformance and the forecasting skills of a LEWS. This isa limitation, because it limits the possibility to compare
the performances of the existing and of future LEWSs.

5 Recommendations
Review of the 26 LEWSs globally allowed to propose a
set of recommendations for the further development andimprovement of geographical LEWSs, and to increase
their reliability and credibility. The recommendations
cover six main, equally relevant areas, namely: (i) design,
deployment and operation of LEWSs, (ii) collection andanalysis of relevant landslide and rainfall data used todesign, operate, and validate the LEWSs, (iii) landslide
models and advisories used in the LEWSs, (iv) LEWSs
evaluation and performance assessment; (v) LEWSs
operation and management, (vi) communication anddissemination.

3.2 Types of landslide early warning systems
Conceptually, LEWSs can be divided in two main, nonexclusive groups. A first group consists of systems that
exploit the early detection of a landslide event when theevent is already occurring e.g., a monitored landslide ismoving. The second group encompasses systems that
attempt to anticipate the occurrence of a landslide event.
LEWSs in the first group relay typically on direct orindirect measurments of physical properties of the moving
mass e.g., surface or sub-surface movements of alandslide [31, 32], seismic (acustic) noise produced by amoving debris flow in a channel [33, 34]. LEWSs in thesecond group use measurments of physical quantitites
considered to be related to the (possible) landslide
occurrence (e.g., rainfall) to inform empirical [35-37] ofphysical [38-43] predictive models.
Another possible subdivision of LEWSs refers to thenumber of landslides that a system is designed (orexpected) to anticipate – and, hence, the extent of thegeographical area within which the systems operate.
LEWSs are designed and operated to (i) monitor, early
detect, and warn about the movement of a single (typically

5.1 Design, deployment and operation
Concerning the design, deployment, and operation of
LEWSs, and based on the evidence that regional andnational LEWSs cover a small part of the areas wherelandslides are expected, globally [3], the main
recommendation is that it is important to increase thecurrent (slow) rate of LEWSs deployment, implementing
new LEWSs where landslide risk to the population is highand fatal landslide are frequent. It is equally important tomaintain existing LEWSs, extending their operationallife.
5.2 Landslide and rainfall data
Regarding the collection and analysis of relevantlandslide and rainfall data to design, operate, and validate
the LEWSs, a critical recommendation is to collectaccurate information on the time of occurrence of2

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landslides. This basic information is only apparently
simple to collect [44-47], and a small landslide catalogue
may result in large uncertainties, and in potentially
inaccurate, or wrong threshold models [48, 49]. It is also
important to establish strategies for landslide and rainfall
data collection, to improve the quantification andcomparison of landslide triggering rainfall fields, and toaddress – as much as possible – the inherent
incompleteness and non-stationarity of landslide andrainfall records. These issues can possibly be mitigatedusing multiple sources of landslide and rainfall
information.

LEWSs scientists and practitioners to decide on, anddisseminate open standards for LEWSs.
With this respect, it is worth noticing that on
December 14, 2020, the – relatively small – communityof scientists and professionals who design, implement,
and manage landslide early warning systems joined toinitiate LandAware [https://www.landaware.org/], aninternational network whose scope is to “shareexperiences, needs, and innovations among experts and todevelop and promote guidelines and best practices” forexisting and new landslide early warning systems [51].

6 Concluding remarks

5.3 Landslide models and advisories
Concerning the landslide prediction models, and theadvisory systems and messages used in the LEWSs, thereview revealed the lack of accepted standard methods forthe defininiton of the landslide threshold models [26, 28,37]. With this respect, the main recommendation is todecise and use open criteria to decide the number of thethresholds, and to adopt sound probabilistic approaches
for landslide models, forecasts, and advisories. It is also
important to explain how information on landslide
susceptibility is used, or it can be used in operational
LEWSs, and to investigate the role of climate andenvironmental changes on the landslide forecast models[50].

Envisioned almost five decades ago [16], thanks tosignificant scientific and technological advancements,
landslide prediction and early warning are now possibleat all geographical scales, from local to global. As a result,
LEWSs are becoming potentially valuable resources forlandslide risk mitigation. This is particularly relevant as
ongoing and projected climate and environmental
changes are expected to increase landslide risk, andparticularly the risk posed to the population by rainfallinduced landslides [50]. However, LEWSs are complex
and multi-faceted systems that require care in their design,
implementation, operation, and management. To avoid
unnecessary failures that can lead to loss of credibility andlegal liability and consequences, it is critical that thecommunity of scientists and professionals who design,
implement, and operate landslide warning systems takes
all necessary precautions, guided by rigorous scientificcriteria and practices [24]. To what extent the community
will be able to do this will depend laregely on thecommunity itself.

5.4 Evaluation and performance
The review outlined the lack of standards to evaluate thelandslide forecast models. In this area, recommendations
are multiple, and include using open criteria for theevaluation of the LEWSs skills and performance;
evaluating all parts of the LEWSs, and not only theoutcome; adopting optimization procedures to decide andrevise the advisory levels; and assess the consequences ofusing external or general advisory schemes. It is also
important to consider the – often unknown and difficult toquantify – uncertainties inherent to the landslide models,
forecasts, and advisories, and the lack of landslide
information when evaluating the LEWSs forecasts andadvisories.

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