APPENDIX G – LANDSLIDES AND RAINFALL: CASE
by M G Winter, I M Nettleton and J A Parsons
Systems to forecast conditions likely to lead to debris flows
have been developed for many regions of the world. In this section
a selection of case studies is presented. These have been selected
to illustrate specific points and on the basis that information on
them is relatively easily available.
Flentje and Chowdhury (2006) describe an observational approach
to continuous real time monitoring of landslides in the Wollongong
city area. Their work encompasses the monitoring of individual
slopes, for which the development of pore water pressures and mass
movement are related to site-specific measured rainfall. In
addition, five stations measuring rainfall, among other parameters,
have been established within the Wollongong area (approximately
25km by 15km) to enable alerts to be broadcast in response to
rainfall events likely to lead to landslides.
Flentje and Chowdury (2006) represent the intensity, frequency
and duration (IFD) of the local rainfall record and they compare
this to the threshold proposed by Caine (1980), and reported here
as Equation 10.1A, in Figure G.1.
Figure G.1 – Rainfall intensity, frequency and duration
analysis of the historical record for a rainfall station in the
Wollongong area (from Flentje and Chowdhury, 2006).
Flentje and Chowdhury (2006) have developed both site-specific
and regional rainfall triggering thresholds, primarily for
deep-seated landslides. The site-specific data is of lesser
interest in the current context, but the regional threshold is of
considerable interest. Their work involved the spatial and temporal
distribution of rainfall that occurred during and prior to an
extreme event during August 1998. Data from a total of 147 rainfall
stations (including 36 pluviometers) within the region have been
analysed and interpolated to give the cumulative rainfall at each
The spatial distributions of cumulative rainfall over different
antecedent time periods were analysed. The antecedent time periods
of six hours and 12 hours prior to 0700 hours on 7 August and 1, 3,
5, 7, 30, 60, 90 and 120 days prior to 0900 hours on the 17, 18 and
19 August were considered in various analyses. Figure G.2 shows the
rainfall intensity-durations for each antecedent rainfall period as
a series of 142 data points making up each of a series of vertical
columns of data points – each vertical column represents one
antecedent period and each landslide recorded is represented by one
data point in each vertical column.
Figure G.2 – The lower bound intensity-duration
‘regional landslide triggering rainfall threshold’ for
the city of Wollongong during the extreme August 1998 event (from
Flentje and Chowdhury, 2006).
The red curve extending across the graph near the base of each
vertical column of data points represents the lower bound
intensity-duration ‘regional rainfall threshold’ for
the city of Wollongong for the August 1998 event. The authors
emphasise that their threshold is for the Wollongong area and may
not be applicable to other areas. In particular their work
considers the particular morphology of the flows and slides that
are experienced in their areas. They also emphasise that the
regional threshold may be at significant variance with that for
individual landslide sites.
They state that the shorter duration thresholds (six hour to
three days for their study area) are most relevant for shallow
debris flow and that this is supported by the work of Wieczorek
(1987) amongst others. However, this may be seen as something of a
simplification as Wieczorek actually states that 28cm of antecedent
rainfall was required before debris flows would be triggered.
Leventhal and Walker (2005) also note that rainfall is a key
trigger factor in the Australian Geomechanics Society (AGS, 2000)
system for landslide risk assessment and management.
G.2 HONG KONG SAR
This case study summarises the methods of collating rainfall
data used in Hong Kong and how rainfall data has been used to
develop a landslide warning system. The Geotechnical Engineering
Office (GEO) has been particularly active in investigating the
distribution, nature and probable causes of landslide occurrences
in natural terrain, and in assessing the hazards of such events. To
describe all of their studies and how they interact is however
considered outwith the remit of this case study. Instead, only
those studies which directly impact on the rainfall correlation
with landslides are described. The historic papers collected into a
volume to commemorate 30 years of slope safety practice in Hong
Kong together provide a comprehensive account of the work
undertaken in that part of the world (Anon, 2007b).
While much of this section discussed the rainfall threshold work
in Hong Kong for natural terrain landslides, it is important to
note that as yet the landslip warning system used in Hong Kong
takes into account only the rainfall threshold analyses of man-made
G.2.1 Key Dates
Early-1970s: Two man-made slopes collapsed on 18 June,
killing a total of 138 people: 71 at the Sua Ma Ping Estate in
Kowloon (Figure G.3) and 67 at Po Shan Road, in Mid-Levels on Hong
Kong Island (Figure G.4). In 1976, another failure at Sau Mau Ping
killed a further 18 people.
Figure G.3 – Sua Ma Ping Estate landslide, 18 June
1972. A 40m high road embankment collapsed after 232mm of
1977: The Geotechnical Control Office (now called the
Geotechnical Engineering Office, GEO) was formed and development of
the Landslip Preventative Measures (LPM) Programme begun. The LPM
programme was developed to inspect, and produce an inventory of,
all man-made slopes and retaining structures in Hong Kong, although
‘special projects’ involving natural terrain studies
were also undertaken. Substandard slopes were, and to some extent
are still being, systematically upgraded to progressively reduce
the landslide risk from man-made slopes which affect the community,
whilst also trying to improve aesthetics of the slope. A major
exercise to ‘educate the public’ of the dangers of
landslides was also begun.
Mid-1979: GEO undertook a mapping exercise of Hong Kong
to identify areas of colluvium, which led to a systematic terrain
classification based on i) slope gradient; ii) terrain component
(hillcrest, foot slope, side slope, etc); and iii) erosion and
instability. This later became known as the Geotechnical Area
Studies Programme, GASP.
Figure G.4 – Po Shan Road Landslide, Mid-Levels, Hong
Kong Island 18 June 1972. The landslide occurred on a steep
hillside above a temporary excavation and demolished a 12-storey
Early-1980s: GEO started collecting and reviewing data
and producing annual reports (since 1984) of rainfall and
landslides in Hong Kong. Rainfall gauge coverage significantly
improves from 1985, and in addition rainfall data also began to be
collected at five minute intervals (throughout the year).
1994: GEO started using consultants to design and supervise
construction of LPM, and to investigate and report on selected
1995: GEO commenced the Natural Terrain Landslide Study (NTLS)
(Evans et al., 1999; Ng et al., 2003). This formed
part of a series of integrated studies to investigate the
distribution, nature and probable causes of natural terrain
landslides and to assess the hazard from such events. Phase 1
produced the Natural Terrain Landslide Inventory (NTLI) (King,
1999) from a review of high level aerial photographs taken between
1945 and 1994. Phase 2 used GIS to examine the spatial distribution
of landslides with respect to geology, slope angle, geomorphology,
vegetation and slope aspect, etc, to determine causal factors and a
preliminary assessment of hazard. Of these, geology and slope angle
were found to be most important in determining natural terrain
landslide susceptibility at a regional scale. Phase 3 produced
regional natural terrain landslide susceptibility and hazard maps,
together with detailed studies of some areas with a high incidence
of landsliding that are close to existing or proposed developments.
Phase 3 also developed procedures for the hazard and risk
assessment of natural terrain in Hong Kong, the investigation of
hydrological and hydrogeological influences on landslide
susceptibility and the continued study into the nature, occurrence
and frequency of exceptionally large natural terrain
The Slope Safety Technical Review Board was then established.
This comprises a panel of three to four renowned experts who
interact extensively with GEO, reviewing and advising on various
aspects of slope engineering.
1999: The National Landslide Inventory (NTLI) was formed (King,
1999) containing information on more than 26,700 landslides on
natural terrain. The associated ‘Landslide
Investigation’ methodology was developed (in conjunction with
Professor Norbert Morgenstern of the University of Alberta in
2000: Landslide Investigations became part of the LPM
2004: The Landslide Potential Index was developed – this
measures the relative severity of a rainstorm relative to its
potential to cause landslides.
G.2.2 Existing Rain Gauge Network in Hong Kong
Rain gauge networks are operated by four separate bodies
including the Hong Kong Observatory (HKO), the Water Supplies
Department, the GEO of the Civil Engineering Department, and the
Drainage Services Department (DSD) of the HKSAR Government.
Altogether these four departments are responsible for operating and
maintaining more than 200 (as of 2001) of the rainfall, tidal and
hydrological gauging stations in the territory.
The rain gauge stations are automatic telemetric stations that
transmit data at five minute intervals throughout the year, during
both the wet and dry seasons. Telemetric readers in Hong Kong are
generally powered from mains electricity as the majority are in
built up areas. However, each station has a 72 hour backup battery
power in case of a supply failure and a number are now self-powered
through solar power and wind power. In Hong Kong it is also
necessary to protect the equipment from extremes in temperature
(80°C in summer inside equipment cases) and humidity (95%
A typical Hong Kong gauging station may contain the following
i) Data logger.
ii) Rainfall gauge.
iii) Telemetry connection.
iv) Incoming power supply.
v) Backup power for at least three days.
vi) Lightning protection system (unlikely to be required for the
vii) Ventilation fan controlled by thermostat (again unlikely to
be required for the Scottish situation).
Examining items (i) and (ii) above in more detail:
i) Data Logger – this would tend to be a programmable
logic controller (PLC) or a remote terminal unit (RTU). The PLC is
easier to install, programme and support but the RTU has superior
communication capability, more memory, and is normally designed for
extremes in temperature and humidity. The information is
transmitted to a central location, generally a PC, via data
link/dial up or via wireless transmission. If there is a break in
the transmission or an equipment problem, the stored data will be
automatically re-transmitted to the office in the next available
transmission. Stored data can also be retrieved from site at any
time. The data are all in text (ASCII format) for easy transmission
and reading. Once the text is received, it is saved in a database
such as Oracle or MS SQL. The latter is preferred as it is
compatible with Excel™ from which graphical representations
(e.g. bar charts) of the rainfall readings can be produced.
ii) Rain Gauge – this would tend to be a Casella tipping
bucket, which tips when the rainfall depth reaches 0.5mm. A 0.2mm
tipping bucket may be more suitable for a non-tropical (Scottish)
situation. In locating a rain gauge the following rules of thumb
- The rain gauge should be positioned on a reasonably level and
- There should be no obstructions in the vicinity. Normally, the
height of any object should be less than 1/4 to 1/3 of the
horizontal distance from the bucket.
- The rain gauge should be positioned to avoid tall buildings and
trees as these can cause eddies which may affect the amount of rain
- Areas that may be susceptible to flooding should be
- The rain gauge should be positioned in an area where the
discharge water from the gauge can drain away quickly.
G.2.3 Determination of a Rainfall Threshold
Initially, correlations of rainfall intensity with landslide
activity in Hong Kong concentrated on failures of man-made slopes,
as these are incidents that tend to affect developed areas and are
therefore reported. There is general agreement that it is possible
to define rainfall threshold above which failures of man-made
slopes increase in frequency (Lumb, 1975; Brand et al.,
1984; Au, 1993; Premchitt et al., 1994).
Thresholds for natural terrain landslides are not so easy to
derive, and have not as yet been implemented, as the failure
mechanisms may differ and records of events are harder to obtain.
However, given that 60% of the land area of Hong Kong is classed as
‘Natural Terrain’ and the ever increasing demand for
land pushes new developments and infrastructure closer to the
natural terrain, the GEO realised the need to get a better
understanding of landslide susceptibility. Hence the Natural
Terrain Landslide Studies were set up as a special project, within
the LPM programme of works, part of which looked at the correlation
between rainfall and natural terrain landsliding.
Evans (1996) was the first to look at the distribution of
rainfall over HK and noted that annual rainfall is not uniform,
even when expected elevation effects are taken into account. The
coastal periphery, outlying islands and the northern New
Territories appear to be significantly drier than elsewhere. This
led to the suggestion that absolute rainfall thresholds for
landslides on natural terrain may also vary across Hong Kong, all
other factors being equal. ‘Normalised’ rainfall, in
which rainfall at a site is recorded as a proportion of the mean
annual rainfall at that site, was considered to be a more
appropriate tool for investigating natural terrain landslide
The NTLI allowed Evans (1997) to carry out a semi-quantitative
assessment of possible rainfall thresholds (Annex G.1). The method
adopted is summarised by Ko (2005) and included as Appendix B for
information. Firstly, he looked at aerial photographs for the
period between 1985 to 1994 (corresponding to the time when spatial
rain gauge coverage was significantly improved) to locate and
record natural terrain landslides, from which he produced a series
of 1:100,000 plans for each year (1985 to 1994). He then plotted
isohyets (lines on a map connecting points that receive equal
amounts of rainfall) of the rolling 24 hour rainfall for all
significant rainstorms for the same period and superimposed these
on the 1:100,000 landslide plans. (Most of this information was
obtained from the annual rainfall and landslide reports produced by
The plots of rainfall and landslides were examined and for each
landslide the maximum rolling 24-hour rainfall in the year of
occurrence was recorded. This figure was reduced to a normalised
value by dividing it by the approximate mean annual rainfall at the
landslide site. A major limitation of this process was obviously
that the maximum recorded rainfall may not necessarily have
triggered the landslide.
Evans found that there were three points of abrupt change in the
gradient (Figure G.5), which were taken as rainfall thresholds
where significant increase in the number of natural terrain
landslides would occur. Examination of his plots of annual rainfall
and landslide distribution showed that for the majority of Hong
Kong, where mean average rainfall is in the range 2,000 to 2,400mm,
landslide densities of 1 per km2 or more are usually
associated with 24 hour rainfall maxima of at least 200mm (0.09
normalised or 9% of annual mean precipitation), while higher
densities of over 10 per km2 tend to be associated with
24 hour maxima of at least 400mm (19% of mean annual
precipitation). It should be noted that these thresholds were
average values, and did not take into account any contributing
factors such as geology, slope, etc. He defined approximate
landslide densities as the following:
a) Low density – less than 1 landslide per
b) Medium density – 1 to 10 landslides per
c) High density – over 10 landslides per
Figure G.5 – Cumulative percentage of natural terrain
landslides against normalised maximum rolling 24-hour rainfall
(1985 to 1994).
G.2.4 Landslide Warning System
The GEO manages and operates the Landslip Warning System with
the Hong Kong Observatory (HKO). Landslip warnings are issued by
the HKO in consultation with GEO when the recorded and forecast
rainfall meets the warning criteria. It is important to note that,
as of December 2007, the warnings were based upon man-made slopes
and not on natural terrain landslides.
The existing Landslip Warning Criterion (Yu et al., 2003)
operates by summing the number of landslide incidents for each of
the vulnerable areas, based on the correlation between landslide
density (number per km2) and rolling 24-hour rainfall of
selected rain gauges. The Landslip Warning level was initially set
at 10 landslides, on the basis that on average about 10% of
reported landslides were major incidents and that casualties were
only caused by major incidents. (This approach is similar to that
described above for the unimplemented natural terrain landslide
The landslide warning system was revised in 2001 following a
review of landslide statistics. This revealed that, whilst on
average major landslides account for about 10% of the total number
of reported landslides, the percentage of major landslides was not
constant but increased with increasing numbers of landslides (i.e.
the percentage of major landslides increased with increasing size
of storm event). For smaller rainstorm events, or at the early
stages of larger events, the ‘first’ major landslide
often occurred after about fifteen landslides were reported to GEO.
Therefore, the warning level was increased from 10 to 15 predicted
landslides in October 2001.
The action levels for the issuing of Landslip Warnings are as
i) Consultation Level – consultation between HKO and GEO
begins when 10 or more rain gauges record rolling rainfall of more
than 100mm in 24 hours.
ii) Alert Level – this is a situation wherein continued
monitoring of rainfall, and liaison, takes place. This level arises
when the average rainfall required to reach ‘warning
level’ is less than 100mm in 24 hours.
iii) Warning Level – Landslip Warning issued by HKO after
consultation with GEO. The rainfall level has achieved that set for
15 or more predicted landslides.
Following recommendations made by Pun et al. (1999), a
performance review of the Landslip Criteria is continuously
undertaken. Improvements are made to take into account the
experience gained from the operation of the system and correlations
between landslide and rainfall are refined.
It is also of interest that the Hong Kong Observatory also
operates a Rainstorm Warning to alert the public to heavy rainfall
events. It should be noted that the Landslip Warnings are
independent from the Rainstorm Warnings, which are set at Amber,
Red and Black for 30mm, 50mm and 70mm of rain in 1 hour expected
within 24 hours respectively. More emphasis is placed on the
rainstorm warnings by the press and TV and during ‘Black
Rain’ events, schools and offices are closed, which has led
to some complaints about loss of profits from some business
sectors. However, on the whole, both types of warnings are well
received by the public.
G.2.5 Further Developments and Proposals for Future Studies
in Hong Kong
Evans’ (1996; 1997) studies were recently updated in 2005
by Ko (2005), to include landslide data up to the year 2000 (an
increase of 75% in the number of landslides), and used
geostatistical analyses and GIS to process and analyse data, thus
removing human error and improving efficiency and accuracy. Ko
concluded that the plots and thresholds produced by Evans had
limitations in the establishment of landslide warning criteria
because they looked at maximum rolling 24 hour rainfall recorded in
a year and not during a storm event. Ko subsequently used
statistics to correlate the year-based 24 hour maximum to a
storm-based maximum (the reader is referred to Appendix D of Ko,
2005). It is unclear, however, if the landslip warning system has
been reviewed in light of her findings and recommendations.
Ko recommended that further refinements were achievable through
the use of GIS. These refinements would include the effects of
elevation (by locating rain gauges in higher natural terrain),
terrain attributes (geology, slope gradient, etc) and terrain
susceptibility classification into their rainfall-natural terrain
landslide correlation. She also recommended other methods of
looking at rainfall data including, the following:
i) Other means of normalisation of rainfall (using rainfall
return period instead of the mean annual rainfall at a given
ii) Using different durations of rainfall (a maximum three hour
rolling with antecedent 30 day rainfall) instead of the 24 hour
iii) Formulation of a natural terrain landslide warning
The only ‘measure of success’ that is published
relates to man-made slopes (Anon, Undated; Sun and Evans, 1999).
Since the adoption of the LPM programme, risk assessment
calculations indicate that the overall landslide risk arising from
old substandard man-made slopes to the whole community of Hong Kong
has been reduced to about 50% of the risk that existed in 1977. The
Hong Kong Government’s demanding (but achievable) objective
is to further reduce the landslide risk from old man-made slopes to
below 25% of the 1977 level by the year 2010.
To put the risk of natural terrain landslides into perspective
(Wong et al., 2004), of the 50 fatalities recorded between
1980 and 2003, 16 were as a result of natural terrain landslides
and a significant number of these were associated with squatter
areas. The historical natural terrain landslide data indicate that
the landslide risk from natural hillsides is lower than that from
man-made slopes in Hong Kong. However, the data may not fully
reflect the inherent landslide risk to the community. Some
landslides were ‘near miss’ incidents that could well
have resulted in more serious consequences and the situation will
only worsen as more new developments take place on, or close to
steep natural hillsides.
The Hong Kong Government’s preferred approach is not to
carry out stabilisation works to large areas of natural terrain,
which would be both impractical and environmentally damaging, but
to mitigate the risk through adjustments to the layout of new
developments and provision of buffer zones and defence measures
(e.g. debris resisting barriers).
A number of case studies have been published describing the
effects of rainfall on landslides in Italy, most importantly a
national system for the real-time prediction of hydro-geological
hazards (floods and landslides). The rainfall detection element of
the system is based on a comprehensive radar network (Casagli,
G.3.1 North Western Tuscany, June 1996
D’Amato Avanzi et al. (2004) report a series of
rainfall induced shallow landslides which occurred on 9 June 1996
in the Apuan Alps in north western Tuscany, Italy. The associated
rainstorm was concentrated over a 150km2 area and 474mm
the rainfall corresponded to 21% of the annual mean.
Some 647 main landslides were recorded and were estimated to
have caused damage to the value of hundreds of millions of Euros,
in addition to causing the deaths of 14 people. The June 1996 storm
occurred after a dry month (17.2mm of rainfall at Pomezzana).
Figure G.6 shows the recorded rainfall at two gauges in the
affected area. At Pomezzana 474mm of rain was recorded in about 12
hours, with a maximum intensity of 158mm/hour, whilst at
Fornovolasco 420mmof rain fell in about 10 hours, before the
instrument was destroyed by either a flood or a landslide. At
gauges some 7km to 10km away only a few millimetres of rainfall was
Figure G.6 – Rainfall data from the 9 June 1996 study
areas: (a) Pomezzana (597m asl) and Fornovolasco (470m asl)
rainfall gauges (from D’Amato Avanzi et al.,
While D’Amato Avanzi et al. (2004) give few
insights into the relations between rainfall and landslides their
paper provides some interesting and useful analyses. For example,
they show that in this area and on this occasion by far and away
the majority of landslides occurred in shallow overburden of
between 0.5m and 2m thick.
G.3.2 Sarno, May 1998
Frattini et al. (2004) describe a series of more than 400
landslides which occurred in May 1998 near Sarno, to the east of
Naples and Vesuvius, in pyroclastic soils. The landslides were
triggered by a storm event and destroyed houses and infrastructure
in addition to killing a total of 159 people. The events broadly
classify as soil slip-debris flows or soil slip-mud flows, with
velocities from very to extremely rapid and with high water content
(Cruden and Varnes, 1996). According to the Pierson and Costa
(1987) classification these would be described as slurry flows
evolving into hyperconcentrated flows, with estimated velocities of
9.3m/s to 10m/s (see Figure 2.3 of Winter et al.,
Detailed rainfall gauge information was not available from
within the authors’ study area, making rainfall analysis very
difficult due to both the high areal variability of intense
rainfall and orographic effects. However, data from five gauges was
reported and Figure G.7 illustrates this data along with the
locations of the rainfall gauges relative to the study area.
Figure G.7 – Cumulative rainfall for 4 to 5 May 1998
recorded by rainfall gauges at Lauro (4.5km north of the study
area; 192m above sea level, asl); S. Pietro (12km east; 215m asl);
Ponte Camerelle (12.5km south; 97m asl); S. Mauro (10.5km south;
31m asl); and Sarno (5.5km south-east; 36m asl) (from Frattini
et al., 2004).
The data from the Lauro gauge was considered to be most relevant
to the events due both to its distance from the hillside initiation
areas and also its position with respect to the path of the storm.
The cumulative rainfall recorded by the Lauro gauge during the 48
hour event was 173mm. The first low intensity fall occurred between
0000 and 0500 hours on 4 May and after a break of 11 hours it
rained continuously until the early morning of 6 May. A maximum
rainfall intensity of 15mm/hour was recorded at 1500 on 5 May and
the mean intensity over the 48 hour period was 3.6mm/hour (Frattini
et al., 2004).
Antecedent rainfall between 28 April and 3 May contributed a
further 61.4mm and the rainfall return period was relatively short,
with a maximum return period of 33 years for the 24 hour rainfall
recorded on 5 May at Lauro. However, this must be set against the
events occurring at the end of the rainy season and if this period
is considered then the return period rises to greater than 100
years (Figure G.8).
The authors maintain that antecedent rainfall played a
significant part in the triggering of this series of landslides,
not least because of the high water retention (up to 100% of dry
weight) of the volcaniclastic deposits. In such case rainfall
infiltration over a prolonged period of time can cause significant
increases in the unit weight making such an effect potentially more
significant than in some other materials.
The rainfall and other data acquired by Frattini et al.
(2004) were used to drive a hydrological model and there is no
evidence that this has been used in any way to attempt to forecast
future events. Indeed, Frattini et al. stated that they
believe that such hydrological models were impractical for reliable
physically-based distributed modelling, largely due to their
complexity, associated data requirement and the difficulties
associated with calibration.
Figure G.8 – Antecedent and event rainfall at the S.
Pietro gauge, 215m asl and 12km to the east of the study area. The
inset upper left shows the daily rainfall for late-April and
early-May (from Frattini et al., 2004).
Sirangelo and Braca (2004) studied the same area as Frattini
et al. (2004), but from a substantially different viewpoint.
Their work involved the creation of a hydrological model, based
upon a back analysis of the May 1998 events. The model produced is
highly complex and comprises two parts:
- ‘Rainfall-Landslide’ for correlating precipitation
and landslide occurrence, intended for model calibration through
the reproduction of historic events.
- ‘Stochastic Rainfall’ for real-time forecasting of
The model has been operated using data from the Sarno events and
predictions performed over a period of approximately four years.
The model enables three levels of elevated landslide potential
status to be implemented, as follows:
- Attention status: with real time monitoring of instruments
(when the mobility function, dependent upon the antecedent
rainfall, reaches 40% of its critical value).
- Alert status: involving civil protection agencies (when the
mobility function reaches 60% its critical value).
- Alarm status: involving the evacuation of the local population
(when the mobility function reaches 80% its critical value).
During the period October 1999 to May 2002, 21, five and one
respectively of each of the above status levels were
The ‘Rainfall-Landslide’ model is currently being
used as a warning system for the Sarno area by the local
authorities. However, it would appear that no events have as yet
been successfully forecast using the system.
G.3.3 Imperia Province, Western Liguria, November
From mid-October to 22 November 2000, the Western Liguria Region
(Figure G.9) experienced prolonged and intense rainfall, with
cumulative values exceeding 1,000mm in 45 days. This was followed
on 23 November by a high intensity storm of 180mm of rain in 24
Figure G.9 – Cumulative rainfall distribution for 23
November in Imperia Province. The grey lines show the extent the
post-event aerial photography. Black dots show the locations of
rainfall gauges. Irregular black lines show the locations of
landslides, which have been exaggerated for illustration purposes
(from Guzzetti et al., 2004).
More than 1,000 landslides, including debris flows and a few
large complex slides, were triggered causing severe damage to
roads, private homes and agriculture as well as leading to three
deaths. The landslides commenced between eight and 10 hours after
the start of the storm and the most intense areal landslide
activity occurred as a consequence of rainfall intensities of
8mm/hour to 10mm/hour (Guzzettti et al., 2004). Mean annual
precipitation ranges from between 750mm and 1,250mm in the west to
between 1,350mm and 1,850 in the central and eastern parts of the
Figure G.9 relates the spatial distribution of cumulative
rainfall in Imperia Province to landslide activity. Although this
Province has experienced less rainfall and fewer landslides than
others within Liguria Region. The map shows that the highest
intensity rainfall coincides with the area in which landslides were
Figure G.10 shows patterns of rainfall intensity versus duration
for a gauge at Imperia (Figure G.10a) and the synthetised rainfall
pattern constructed for San Romolo (Figure G.10b), the latter based
on a cumulative rainfall fof 241.2mm (i.e. at the San Romolo gross
measurement gauge) and the same intensity as recorded at the
Imperia gauge. Each graph begins at 15 minutes (0.25 hours) at the
left hand side of the graph and ends at 28 hours on the right hand
side. The times of landslide occurrence as observed at nearby
Ceriana are over-plotted. Figure G.10c corrects the timings of
landslides for a two-hour apparent lag time observed between the
highest intensity rainfall at Imperia and Ceriana.
G.3.4 Piedmont Region
In dealing with debris flows and soil slips triggered by short
intensity storms in the Piedmont Regionof NW Italy, Aleotti (2004)
usefully defines some of the key rainfall parameters relating to
the potential to trigger landslides (Figure G.11).
Aleotti (2004) proposes an equation similar in form to equation
(10.1A) as follows:
This equation is claimed to account for 90% of the available
data for which rainfall is believed to have led to landslides in
the Region. It has been refined by normalising the intensity of the
rainfall (NI) with respect to the mean annual precipitation
(MAP) such that two equations collectively describe the
triggering threshold, as follows:
where the normalised intensity (NI) is expressed as a
percentage by I/MAP ¥ 100.
Finally, Aleotti (2004) expresses the critical normalised
intensity in terms of the normalised critical rainfall (NCR) to
encompass 90% of events studied, as follows:
where the NCR = R/MAP ¥ 100.
Aleotti (2004) used hourly rainfall in the study, but appears to
have analysed only the storm events taking no account of
longer-term antecedent rainfall perhaps accounting for some of the
poor correlations reported.
Figure G.10 – Landslide timings at Ceriana relative to
intensity-duration plots: (a) rain gauge at Imperia; (b)
synthetically derived rainfall at San Romolo; (c) synthetic San
Remolo data corrected for a two hour time lag (from Guzzetti et
Figure G.11 – Definition of rainfall parameters (from
In recent years there have been a number of debris flow events
that have exposed the population of the Cancia area of the
Dolomites to significant risk. In response, an alarm and monitoring
system was set up with data from three rain gauges being monitored
during debris flow events.
Data from the rain gauges was analysed, taking into account the
elevation of the gauges, to determine debris flow initiation and
rainfall relations. The findings were then compared with results
from geologically similar areas in the Eastern Alps.
The geology of the area is typically Triassic to Jurassic of the
Dolomitic stratigraphic sequence. The deposits that have proved
susceptible to debris flows are gravels with a low content of sand
and fine particles.
The climatic zone is a cold Alpine Climate (Köppen Class D)
with an annual rainfall of 1,000mm, which falls mainly in spring
The drainage basin for the Cancia debris flow area covers a
surface area of approximately 1.8km2, and the profile of
the debris flow channel is shown in Figure G.12. Debris flows are
recorded from 1868 (100,000m3) to 1996 (40,000
m3 to 45,000m3), with activity over period
1986 to 1996 being one event every 1 to 2 years.
Figure G.12 – Longitudinal profile of a flow channel,
the upper part of the source area and mean slope angles in the
different sectors (from Bacchini and Zannoni, 2002).
Thresholds based on Ceriani et al. (1994) were found to
be too high, with most of the observed events falling in the stable
zone (Figure G.13). Thresholds were developed for debris flows in
terms of mean intensity (I), duration (D) and mean
annual precipitation (MAP). These utilised normalised rainfall and
normalised intensity expressed as a percentage of the MAP (Figure
Figure G.13 – Normalised rainfall intensity
(intensity/MAP) versus duration and debris flow correlation. The
dashed line shows the debris flow threshold proposed for the study
area (from Bacchini and Zannoni, 2002).
Thresholds for debris flows, written in terms of the normalised
rainfall (Rn = R/MAP) were as follows:
where I > 2 mm/hour.
Normalised rainfall and normalised rainfall intensity should
only be used in limited areas where the annual frequency of rain
storms is fairly constant (Wilson, 2000).
Typically, triggering rainfall events were found to be 20mm to
30mm in 1 or 2 hours (i.e. not particularly high rainfall levels)
but due to the short duration relative to the data reading
frequency they may be of intermediate intensity. The role of storm
cells in defining rainfall intensities leading to potential debris
flow conditions is thus clear.
Rainfall thresholds were found to be an unsuitable medium for
the purposes of debris flow prediction but useful in determining a
suitable level at which actions by management and monitoring
personnel might be undertaken as part of an overall management
Landslides are a common occurrence and a recurring problem on
the mountainous island of Jamaica (R Ahmad, 2006; Personal
Communication, 2006). These are usually associated with tropical
storms, including hurricanes, the paths of which often pass close
to the island. Typically, disruption and damage takes a number of
- Severance of transport routes leading to stranded
- Loss of income through economic activity, including loss of
productive agricultural areas, especially coffee farms and
farm-to-market access roads.
- Closed schools.
- Damage to property and community facilities.
- Interruption to domestic water supplies.
- Addition of sediment to river profiles raising channel levels
and thus increasing future flood hazard.
In particular the social fabric of communities may be severely
disrupted by many of these consequences and, in addition,
individuals are exposed to the trauma of evacuation and the loss of
their homes. Much of the impact of such landslides is due to
transported landslide debris, especially along debris chutes and
deposition areas, which may often be far removed from the landslide
Ahmad (2003) reports the development of two thresholds:
- For debris flows that commonly develop from shallow landslides
during intense rainfall.
- For deep-seated landslides that are usually triggered by
Also noted is the fact that rainfall amounts for storms that did
not trigger landslides are equally important in that they allow the
population of the threshold graph from both directions. The
threshold established by Ahmad (2003) is presented in Figure
Figure G.14 – Rainfall intensity-duration threshold for
shallow landslides in eastern Jamaica, using data from 19 storms
between 1951 and 2002 (from Ahmad, 2003).
Ahmad (2003) notes that the rainfall threshold relation is
defined for storm durations between 1 and 168 hours and average
rainfall intensities between 2 and 93mm/hour. The threshold
relation indicates that, for rainfall of short duration (about 1
hour), intensities greater than 36mm/hour are required to trigger
There is a relation between landslide characteristics and the
position of the landslide-triggering storm on the threshold line.
Storms near the short-duration/high-intensity end of the threshold
line trigger mostly shallow landslides (e.g. Figure G.15) by
causing an excess pore pressure in shallow colluvial zones.
In contrast, storms near long-duration/low-intensity end of the
threshold have triggered the largest, deepest landslides in eastern
Jamaica (e.g. Figures G.16 and G.17).
Figure G.15 – Shallow landslide induced by rainfall
between Ramble and Somerset on the Yallahs River in St Thomas
Parish, eastern Jamaica. The road followed the shoulder of the hill
to either side of the landslide.
Figure G.16 – Deep rainfall induced landslide on the A2
road between Whitehall and Martins in St Mary Parish, eastern
Figure G.17 – Deep rainfall-induced landslide on the B1
road at Section in Portland Parish, eastern Jamaica.
Landslides in Nepal are often associated with high intensity
rainfall in combination with the highly active slope processes
that, in such an active mountain environment, are driven by
gravity. Monsoon rainfall patterns mean that more than 80% of the
annual rainfall occurs within a four month period between June and
September, with the 50-year average for Kathmandu in July being
around 375mm. At the Arughat Bazar rainfall gauge (near the Privthi
Highway, H04: Figure G.18) in excess of 550mm of rain fell in
August 2000; while the highest recorded rainfall in a 24 hour
period was at Kulekhani, where 540mm of rain fell on the 19 and 20
July 1993, an average of 22.5mm/hour. Sunuwar et al. (2005)
compare this to figures reported by Wieczorek (1996) of 6.3mm/hour
for the triggering of landslides in California.
Rainfall-induced landslides are thus frequent and often block
the major roads of Nepal, causing particular problems of the
effects of severance of access for rural populations. There appears
to be no effort to forecast landslides using rainfall data in
Nepal; there remains a suspicion that conditions are sufficiently
extreme that such an exercise might be unproductive in that the
entire monsoon season would be seen as high risk period.
Experience in Norway has indicated that 8% to 10% annual
precipitation in one day (24hrs) is likely to lead to debris flows
in ‘exposed’ (or susceptible) locations (U Domass,
Personal Communication, 2006). If there is significant antecedent
rainfall (several days) then this threshold may be lower.
Figure G.18 – Privthi Highway, H04, Nepal.
An investigation of 30 debris flows in Norway was undertaken by
Sanderson et al. (2005). The work indicates that steep
Norwegian slopes are often partially covered with glacial till,
which in many places is itself covered with colluvium. The silt and
clay content of these is typically in the range of 10% to 30%
(Jorgensen, 1978). The upper 0.5m to 1.0m of soil has high
permeability due roots and organisms, and this enables frost to
influence the structure of the soil profile. The permeability of
the lower soil is much lower.
Norway comprises two climatic areas:
- Marine west coast climate (western Norway), typically 1,000 to
3,000mm annual rainfall falling in predominantly south-westerly
winds during the passage of warm fronts. Daily rainfall can exceed
- Continental sub-arctic climate (eastern Norway), typically 300
to 1,000mm annual rainfall falling predominantly during convective
Slope aspect plays an important role with the greatest rainfall
on windward slopes (south-west facing slopes). The high relief on
the west coast also leads to large differences in precipitation
even over small distances. South-west facing slopes are also most
prone to intense meltwater production due to the exposure to wind
and solar radiation.
Field measurements indicate the presence of slip surfaces along
a relatively impermeable layer at 0.5m to 1.0m depth. This surface
is a boundary between relatively high permeability material and
underlying lower permeability material, leading to increased pore
Climatic monitoring stations in the areas of the 30 debris flows
investigated record the following information three times a day (at
0700, 1300 and 1900):
2. Snow depth.
3. Air temperature/humidity.
4. Wind speed/direction.
Records of precipitation and calculated snowmelt over the 12
hour, 24 hour, 7 day, 15 day and monthly time periods were
assessed. For the continental climatic areas debris flows activity
was found to be most frequent in April and May, whilst for the
marine climate August to December were the most active months. For
the marine west coast climate areas the weather patterns triggering
the majority of events were:
1. Heavy rainfall of one day duration with a concentrated period
of 1 hour to 4 hours.
2. Rainfall in combination with snowmelt over 3 days to 7
Two examples of this are:
- In this example event the 24-hour precipitation in excess of
64mm, with the 24-hour rainfall return period being >150 years.
The period prior to this had been relatively dry, with only 29.5mm
of precipitation over 14 days.
- In the second example event the probable cause was rainfall and
snow melt, yielding 190mm in a week (211% of monthly average)
– a figure corresponding to a return period of several
The resulting intensity-duration relations for the sites studies
were compromised by a high degree of uncertainty, mainly due to
1. The widespread rain gauge network does not cover all local
regions where heavy precipitation is experienced.
2. The frequency of recordings was too low to reflect variations
in precipitation with time – Sanderson et al. (2005)
found that climatic stations recording at 6 and 12 hour frequencies
could not be used for generation of water supply/debris flow
3. The rate of snowmelt depends largely on wind speed.
Caine (1980) plotted rainfall intensity against duration for
worldwide debris flows and found a lower bound as given in Equation
Sanderson et al. (2005) discuss the fact that time is a
very significant factor, with rainfall over as little as one hour
being potentially critical in the generation of debris flow (Figure
G.19). Also identified was a lower intensity-duration threshold
(Figure G.20), derived from the 30 debris flows, and this is
where P is the ‘critical water supply’ expressed as a percentage of mean annual precipitation and D
is duration (hours).
For example, the 12-hour critical water supply expressed as a
percentage of mean annual precipitation is given by:
If the mean annual precipitation is then 2,000mm then the ‘critical rainfall level’, R12hour Crit, is (2,000 ¥ 5.33)/100 =
Sanderson et al. (2005) conclude that debris flows
exhibit the following characteristics:
1. They are triggered by rare climatic events with return
periods of 50 years or more.
2. They show short response times to climatic events (e.g. 4
3. Many recent cases are apparently due to human activity
affecting slope hydrological regime: e.g. forest roads, forest
Figure G.19 - Debris flow trigger due to intense rainfall
within the west-coast climatic region (after Sanderson et
Figure G.20 - Critical water supply for debris flow
initiation. Data points indicate water supply in debris flow events
(after Sanderson et al., 2005).
Toll (2001; 2006) reports on rainfall leading to landslides in
Singapore and presents a graph of the rainfall occurring on the day
of the landslide against that in the five days preceding the
landslide (Figure G.21).
Figure G.21 – Rainfall events leading to landslides in
Singapore (from Toll, 2006).
While a few minor landslides have occurred after intense one-day
rainfalls with little antecedent rainfall others have occurred with
low one-day rainfall and higher antecedent rainfalls. Toll (2006)
concludes that this indicates that total rainfall, over an extended
period, is more important that either daily or antecedent
The solid diagonal line in Figure G.21 represents a total
rainfall of 100mm in a six-day period appears to define the minimum
rainfall conditions that can lead to minor landslides in
Mikos et al. (2004) report on a study of two debris flows
that occurred near Stoze in NW Slovenia on 15 and 16 November
A rain gauge at the nearby village of Log pod Mangartom recorded
1,638mm (more than 60% of the average annual precipitation) in the
48 days leading up to the events (average rainfall intensity
1.42mm/hour), corresponding to a return period of more than 100
years. Other rainfall depths for shorter durations within the same
time window (481.6mm in 7 days, 174.0mm in 24 hours, 70mm in 1
hour) had recurrence intervals of much less than 100 years (Table
Several short periods of intense rainfall events were recorded
in Log pod Mangartom during 2000, as follows:
- 407.4mm (11 to 13 October).
- 380.2mm (14 to 16 November).
- Daily rainfall of 174mm (12 October).
- Daily rainfall of 165.3mm (14 November, a day before the first
These levels of rainfall are not extreme for the area. In
contrast, the precipitation depths for one and two months measured
at the gauge were extreme, with return periods of around 100 years.
Only the measured rainfall intensity of 1.42mm/hour in the last
1,152 hours (48 days) lies outside the collected historical data
for critical rainfall intensity and duration (Crosta, 2004); all
others of shorter duration lie within these
Table G.1 – Measured rainfall depths at rainfall
gauging station in Log pod Mangartom compared with statistical
values given for different recurrences intervals for that station
(reference period 1961 to 1990) (from Mikos et al.,
The comparison with empirical (Caine, 1980) rainfall-intensity
relations shows that all measured data in Log pod Mangartom in
late-Autumn 2000 lie above but close to the lower bound threshold
for shallow landslides worldwide (Equation 10.1A). Only the
rainfall intensity of 70mm/hour measured in a one hour period on
the evening of 16 November 2000 came close to Caine’s upper
bound threshold (Equation 10.1B).
Debris flows are a geomorphological process common in the Swiss
Alps, and in 2000 four significant flows (between
5,000m3 and 35,000m3) occurred which were
monitored by debris flow observation stations. These comprised
video cameras, ultrasonic devices, radar, geophones and rain gauges
(Hurlimann et al., 2003).
The debris flows occurred in the Illbach and Schipfenbach
catchments, both of which appear to be characterised by channelised
debris flow activity.
The Schipfenbach monitoring system incorporated a rainfall gauge
recording every 10 minutes. This indicated a rather dry June period
(Figure G.22a) followed by a high July rainfall of 189mm. During a
3 hour period before the debris flow the maximum intensity was
11mm/hr, yielding a total rainfall for 6 August of 106mm. Comparing
this event to Zimmermann et al. (1997) the authors proposed
a relation between intensity and duration as follows:
Hurlimann et al. (2003) concluded that the threshold was
most likely too high. However, they did establish that the critical
rainfall fell in a period of 4 to 24 hours before the event.
Rainfall gauges were not installed in the Illbach catchment
until after the 2000 events. However, the authors indicate that the
100 year return rainfall intensity is between 35mm/hour and 57mm/hr
for 0.5 hour and 1.0 hour rainfall durations.
Figure G.22 – Precipitation analysis of the
Schipfenbach debris flow. (a) Cumulative rainfall during the 24
hour prior to the debris flow event (the arrow indicates the time
of initiation). Inset shows the daily precipitation during the
month prior to the debris flow. (b) Comparison between the climatic
threshold for debris flow initiation in the outer parts of the
Swiss Alps and the data for the Schipfenbach event (after Hurlimann
et al., 2003). (Note: Equation 2 referred to in Figure G.22a
is reproduced as Equation G.9 in this report.)
The superficial deposits in the Illbach catchment typically
comprise 35% to 40% sand with less than 5% clay. The Schipfenbach
catchment superficial deposits typically comprise 45% to 70% gravel
with a clay fraction of less than 5%.
The authors concluded that:
- The debris flows were triggered by intense rainfall leading to
- Large landslides in both catchments provided debris for
- Ultrasonic and radar measurements were practicable for defining
debris flow hydrographs (channelised debris flows).
- Monitoring indicated a wide spectrum of flow behaviour even
within the same channel.
- A critical factor was the rainfall in a period of 4 hours to 24
hours before the debris flow.
G.10 UNITED KINGDOM
G.10.1 North-West England
A rainfall and early warning system was set up to monitor the
condition of earthworks on the Settle to Carlisle line following a
landslide which caused a train derailment at Ais Gill, Cumbria on
31 January 1995
Rainfall gauges were installed at several locations where
earthworks were classed as ‘Poor’. Hourly, daily,
weekly and 28 day rainfall levels were recorded and trigger levels
set. These trigger levels were based on a study by Lancaster
University of rainfall levels that had caused landslides in
The levels set were as follows:
1. 24-hour total threshold set at 80mm.
2. Antecedent Precipitation Index (API) threshold set at
3. 30-day total threshold set at 300mm.
The system was used to put in place train speed restrictions
when trigger levels were exceeded. The system was removed two years
later when remedial measures had been undertaken on the railway
G.10.2 South-West England
Network Rail (Personal Communication, 2006) report on a system
on trial in southern England incorporating three levels of alert
status, as follows:
1. Earthwork Failures Likely.
2. Earthwork Failures Possible.
3. Earthwork Failures Unlikely.
4. Embankment Desiccation Possible.
The alert levels are based on Soil Moisture Deficit (SMD)
(Figure G.23) and rainfall as a percentage of the Long Term Average
(LTA). The threshold rainfall is defined as 175% of the LTA.
G.10.3 Scottish Highlands
A series of debris flows occurred in the Scottish Highlands
between 1999 and 2001 adjacent to the A890 Stromeferry Bypass road
and the railway which runs on a close by. As the debris flows had
been triggered by rainfall events, a review of existing rainfall
data was undertaken (Nettleton et al., 2005a).
The nearest automated rainfall gauge was at Plockton 10km to the
west, on a low relief peninsula, and was not initially considered
to be representative of the rainfall at Stromeferry. However,
assessment of the 1999 to 2001 daily rainfall data from this gauge
indicated a good correlation of peak rainfall events with debris
flow activity. In particular, the 14-day cumulative rainfall
indicated clear peaks that correspond well with the January 1999
and October 2001 debris flow events and the smaller event of
October 2000, thus indicating the importance of antecedent as well
as high intensity rainfall.
Figure G.23 – Soil Moisture Deficit (SMD) and rainfall
graph (from Network Rail, Personal Communication, 2006).
Figure G.24 shows a graph of the normalized rainfall from the
gauge at Plockton and a Scottish Environmental Protection Agency
(SEPA) river flow-gauging station some 5km north-east of
Stromeferry at the head of Loch Carron. There are good correlations
between both sets of data and debris flow occurrence, probably as
the principal weather fronts track in from the west. This indicates
that the Plockton rainfall is, in fact, representative of the
Stromeferry/River Carron catchments in terms of peak events. The
magnitude of rainfall is however likely to be lower at Plockton due
to its lower relief. There is a rainfall and river flow peak in
November/December 1999 which has no corresponding debris flow
event, but this may be because the main gully in question had a
major clear out in January 1999.
For an early warning system at Stromeferry an automated local
rain gauge, appropriate trigger levels and some form of automated
barrier or signs would be required (Nettleton et al.,
2005a). Figure G.24 suggests that a trigger level for the 14 day
antecedent rainfall could be developed based on the Plockton
rainfall. Similar trigger levels would have to be developed for
daily rainfall and a range of other antecedent rainfall
The current rainfall readings are only daily and the response of
the system to high intensity rainfall events correspondingly would
be limited. Hence, a system recording hourly rainfall would be
required to provide greater response sensitivity to high intensity
events which follow a moderate antecedent build-up.
G.11 UNITED STATES OF AMERICA
Between 1986 and 1995 the United States Geological Survey (USGS)
and the National Oceanic and Atmospheric Administration (NOAA)
undertook an exploratory program for predicting debris flows in the
San Francisco Bay area. Circular 1283 (Anon, 2005) presents the
findings and recommendations of a joint USGS/NOAA task force tasked
with developing a plan for the implementation and operation of a
NOAA/USGS system to issue joint Outlooks, Watches and Warnings for
areas deemed to be at risk from debris flows as a result of current
or forecast precipitation.
Figure G.24 – Normalised graph of 14 day cumulative
rainfall at Plockton and River Carron flow for 1999 to 2001 showing
the major debris flow events (from Nettleton et al.,
The task force reviewed several operational rainfall
intensity-duration landslide warning systems from around the world,
- Hong Kong (Chan et al., 2003)
- San Francisco Bay 1986-1995 (Wilson 1997)
- Rio de Janeiro (1998-2003, 42 warnings) (D’Orsi et
- The State of Oregon (Mills 2002)
- Lyme Regis, UK (Cole and Davis 2002)
- Seattle, Washington (Godt et al., 2005)
The task force identified that an antecedent rainfall threshold
and an intensity duration threshold would be required for a warning
system. To achieve this, methods for quantitative precipitation
estimation (QPE) and quantitative precipitation forecasting (QPF)
The report provides elements of a worked up proposal for the
research and development of a full debris flow warning system.
Wieczorek (1987) studied debris flows in the Santa Cruz
Mountains of California over a 10-year period, including 110 debris
flows triggered during 10 storms. Analysis of the rainfall records
indicated that two conditions had to be met for debris flows to be
initiated: antecedent rainfall had to exceed a minimum threshold,
and the storm rainfall had to exceed certain a level of intensity
for a specified duration.
In the low permeability clay, silt and clayey silt soils of the
study area, antecedent rainfall was found to be important over
periods from seven days to two months. Seasonal rainfall of at
least 28cm was observed prior to any debris flows being triggered.
It was also found that rainfall values during the preceding seven
to 30 days accounted for about 80% of the antecedent seasonal value
and that the seven to 30 day antecedent rainfall values for storms
that triggered debris flow was about twice that of storms that did
not trigger debris flows.
Wieczorek (1987) derived the expression defining the storm
events capable of triggering debris flows, provided that sufficient
antecedent rainfall had fallen, as follows:
where I is the rainfall intensity (in cm/hour) and
D is the duration of rainfall (in hours).
The equation is best defined within the range of intensities
0.5cm/hour to 1.0cm/hour and the relation is assumed to be
asymptotic at its extremes.
Figure G.25a shows a plot of duration for different levels of
intensity for a number of storms and the threshold (Equation G.10)
separates those that did and those that did not trigger debris
flows. Each of these storms followed antecedent rainfall of at
least 28cm. Each storm is represented by a family of data points,
each point corresponding to a duration of particular intensity. In
contrast, Figure G.25b illustrates storms that were not associated
with at least 28cm of antecedent rainfall. While in Figure G.25a
the intensity-duration data sited to the right of the curve defined
by Equation (G.10) generally triggered debris flows and those sited
to the left of the curve did not, in Figure G.25b none of the data
are associated with debris flow activity.
The data presented by Wieczorek (1987) presents a very simple,
threshold-based approach to coping with the effects of antecedent
rainfall. While the intensity-duration approach then used to deal
with the subsequent storm rainfall is potentially difficult to
achieve in real-time this is broadly true for all related
G.11.2 Washington State (Seattle)
The Seattle area experiences shallow landslides in the colluvium
deposits triggered during or immediately following heavy rainfall
or snowmelt. Previous studies in the Seattle area have indicated
that both antecedent and storm rainfall have significant effect.
Seattle has a dense network of rain gauges with hourly recordings
dating back over 25 years. This coupled with records of landslides
(Laprade et al., 2000) has enabled development of empirical
rainfall / slope stability models (Godt, 2004).
Recent analysis of data between 1933 and 1997 showed a
combination of three day triggering rainfall and 15-day antecedent
precipitation can be used to forecast when three or more landslides
can be expected during a three day period (Chleborad, 2003).
The Seattle rain gauge network comprises 17 tipping bucket
gauges providing a dense coverage (2km to 5km between gauges). Mean
rainfall intensity, Imean, and duration,
D, were compiled from rainfall gauge data. A rainstorm was
defined as a period of rain bounded by at least 3 hours of no
rainfall. Analysis of six rainstorms, which triggered shallow
landslides, between 1978 and 1997 yielded a rainfall
intensity-duration graph with a threshold defined by:
Figure G.25 – Intensity-duration data for storms in the
Santa Cruz Mountains in California: (a) data for storms following
28cm of antecedent rainfall; (b) data for storms that did not
follow 28cm of antecedent rainfall (from Wieczorek, 1987).
The authors employed the Antecedent Water Index (AWI),
calibrated with measurements of soil-water content and rainfall to
provide a general assessment of the soil-moisture conditions
A decision tree for assigning warnings was developed based on
the AWI and the rainfall threshold, as shown in Figure G.27.
The authors concluded that, based on landslide events during the
previous 25 year period, the rainfall intensity-duration and the
water balance model would have flagged some 56 rainstorms that
exceeded the intensity-duration threshold, with three rainstorms
below the intensity-duration threshold (‘Null’) which
were associated with evidence of shallow landsliding.
Some 28 rainstorms were assigned a ‘Watch’ status
and evidence of shallow landsliding was noted in 42.9% of these. A
further 13 rainstorms were assigned a ‘Warning’ status
and shallow landsliding occurred in 61.5% of these.
Figure G.26 – Rainfall, volumetric water content, and
the Antecedent Water Index (AWI) for the Edmonds field site for the
period 17 October 2003 to 14 February 2004 (from Godt et
This research was also applied specifically for rail
transportation (Baum et al., 2005). For this application
rain gauges were normally set to record hourly but this increased
to every 15 minutes during times of high precipitation
(>2.54mm/hour). The data were transmitted by radio telemetry
system and graphs were produced on a web server in near to real
For this application the alerts were as follows:
1. Advisory – Days in advance.
2. Watch – 3 hours to 72 hours in advance.
3. Warning – Near real time.
G.12 OTHER REGIONS AND COUNTRIES
Other regional studies of landslide risk assessment that have
been studied in order to obtain information useful to this work
Albania: Bozo et al. (2005) report on landslide
risk assessment for roads and include rainfall events as one of the
seven most important factors in their triggering. Around half of
all landslides in Albania are thought to occur during or just after
‘rainy weather’. It is not, however, entirely clear how
this is translated into an assessment mechanism although it seems
likely that seismic activity is more of a potential trigger than in
Figure G.27 – Decision tree for assigning warnings
(from Godt et al., 2005).
Brazil: Ortigao et al. (2001) report on a system
based upon intensity and accumulated 96-hour rainfall. This system
appears to be adapted more to slower moving landslides that may be
triggered by relatively short periods of rainfall with little or no
influence from longer term antecedent rainfall.
Mainland China: Zhou and Chan (2005) note that the
understanding of debris flow mechanics is at a relatively immature
level of understanding and that qualitative evaluation parameters
currently predominate over quantitative ones. Other recent work on
regional landslide management in China has been conducted by Wen
et al. (2005) and Yin and Wang (2005).
Columbia: Montero Olarte and Ojeda Moncayo (2005) report
that 70% of the Columbian national road network suffers the
consequences of frequent obstruction or destruction due to the
actions of rainfall-triggered landslides and that landslides in
Columbia are mainly triggered by rainfall.
Cuba: Castellanos Abella and van Westen (2005) report on
a proposed landslide risk assessment method for Cuba. Rainfall is
equally ranked with seismic activity as a triggering factor.
Ethiopia: Woldearegay et al. (2005) report on
landslide hazard mitigation strategies for the northern highlands
of Ethiopia. While the authors implicitly acknowledge the role of
rainfall in this region (where the bimodal annual average can vary
between 500mm and 2,000mm their paper pays relatively little
attention to this issue).
United States of America (Alaska): Sidle and Swanston
(1976) estimated a return period of less than two years for a storm
that caused a small debris flow in Alaska. They also noted that
around 54% of the rain fell in the final three hours of the storm
(total duration 10 hours). This early work perhaps points to the
importance of the relation between intensity and duration in
understanding how debris flows are triggered.
ANNEX G.1 – METHOD ADOPTED IN PRELIMINARY ASSESSMENT BY
EVANS (1997) (Extracted from Ko, 2005)