The Influence of the North Atlantic Oscillation on Rainfall ... - IDLCC

Natural Hazards (2005) 36: 331–354 DOI 10.1007/s11069-005-1709-0

Springer 2005

The Influence of the North Atlantic Oscillation on Rainfall Triggering of Landslides near Lisbon RICARDO M. TRIGO1,2,w, JOSE´ L. ZEˆZERE3, MARIA L. RODRIGUES3 and ISABEL F. TRIGO4 1

Centro de Geofı´sica da Universidade de Lisboa, Departamento de Fı´sica, Faculdade de Cieˆncias, Universidade de Lisboa, Campo Grande, Ed C8, Piso 6, 1749-016, Lisboa, Portugal; 2 Departamento de Engenharias, Universidade Luso´fona, Lisboa, Portugal; 3Centro de Estudos Geogra´ficos, Universidade de Lisboa, Portugal; 4Instituto de Meteorologia, Lisboa, Portugal (Received: 22 September 2003; accepted: 7 January 2005) Abstract. The majority of landsliding episodes in the area north of Lisbon are associated with rainfall events of short (less than 5 days) medium (5–20 days) or long duration (more than 20 days). The precipitation regime in Portugal is highly irregular, with large differences between wet and dry years. We have assessed the impact of the North Atlantic Oscillation (NAO) on both the winter precipitation and the timing and magnitude of associated landslide events. Results show that the large inter-annual variability of winter precipitation is largely modulated by the NAO mode. The precipitation composite corresponding to high NAO index presents a considerable lower median value (47 mm/month) than the corresponding low NAO index class (134 mm/month). The entire precipitation distribution associated with the low NAO index composite encompasses a wider range of values than the corresponding high NAO index composite. This non-linear behavior is reflected in the probability of occurrence of a very wet month (precipitation above the 90% percentile) that is just 1% for the positive NAO class and 23% for low NAO index months. Results for the low NAO class are crucial because these months are more likely associated with long-lasting rainfall episodes responsible for large landslide events. This is confirmed by the application of a 3-month moving average to both NAO index and precipitation time series. This procedure allowed the identification of many months with landslide activity as being characterized by negative average values of the NAO index and high values of average precipitation (above 100 mm/month). Finally, using daily data we have computed the return periods associated with the entire set of landslide episodes and, based on these results, obtained a strong linear relationship between critical cumulative rainfall and the corresponding critical rainfall event duration. Key words: NAO, landslides, extreme precipitation, Portugal

w

Author for correspondence: Departamento de Fı´ sica, Faculdade de Cieˆncias, Universidade de Lisboa, Campo Grande, Ed C8, Piso 6, 1749-016, Lisboa, Portugal. Tel: +351217500855, Fax: +351-217500977, E-mail: [email protected].

332

RICARDO M. TRIGO ET AL.

1. Introduction Like many regions located within the Mediterranean belt, Portugal is a country prone to landslide activity. Despite the associated economical losses, the majority of landslide events do not cause human casualties. However, recent episodes of debris flows in the Azores archipelago (1997) and in northern continental Portugal (2001) claimed 29 and 12 human lives, respectively, and brought widespread disruption to roads and buildings. It is widely accepted that high duration/intensity rainfall events are the most important triggering mechanism of landslides worldwide (Wieczorek, 1996; Corominas, 2001). This is also true in Portugal, where recent landslides triggered by snowmelt or earthquakes can be considered negligible. The derivation of a general ‘‘universal rule’’, that takes into account the rainfall thresholds related with landslide activity, has been attempted since the pioneering work by Caine (1980) (Fukuoka, 1980; Crozier, 1986; Jibson, 1989). However, it is now accepted that there are no universal rainfall thresholds that can be associated with landslides (Dikau and Schrott, 1999; Corominas, 2001). Furthermore, the frequency, the magnitude and the type of landslides may be related to different rainfall conditions, thus implying that similar climatological conditions can be easily associated with different patterns of landslide distribution (Van Asch et al., 1999; Polemio and Petrucci, 2000; Zeˆzere, 2000; Corominas, 2001, Zeˆzere and Rodrigues, 2002). In previous works we have discussed and analyzed the triggering factors of landslides in the area North of Lisbon (Zeˆzere et al., 1999a, b; Zeˆzere, 2000; Zeˆzere and Rodrigues, 2002). These studies have shown that most slope movements are associated with clear climatic conditions. In fact, large number of landslides were triggered during the wettest years (more than 500 landslides in 19 particular rainfall episodes) and an almost absolute inactivity was reported throughout the remaining years (Figure 1). The characteristics of the precipitation regime near Lisbon, and the inter-annual variability of precipitation were considered of outmost importance to understand regional landslide activity. The precipitation regime in Portugal is highly irregular in both spatial and temporal dimensions. The distribution of precipitation presents an evident seasonal pattern, with a strong difference between the ‘‘rainy season’’, that extends between November and March and the ‘‘dry season’’, with almost no rainfall, during July and August. April/June and September/ October correspond to the transition months into and out of the ‘‘rainy season’’ (Trigo and DaCamara, 2000). The intra-annual variability of precipitation in Portugal may be explained by the characteristics of the general circulation of the atmosphere in the western part of the Iberian Peninsula. However, though the spatial distribution of rainfall, as well as its seasonal variability, may be explained in terms of the global circulation

NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES

333

Figure 1. Annual precipitation (climatological year) distribution at S. Juliaõ do Tojal (reference rain-gauge) from 1956/1957 to 2000/2001. The horizontal line indicates the mean annual value (MAP) and the vertical arrows indicate those years where landslide events were reported.

and regional climate factors (e.g. latitude, orography, oceanic and continental influences), this is not true for the inter-annual variability. Most of the precipitation during the wet winter season can be explained in terms of a relatively small number of large-scale atmospheric modes at the monthly scale (Rodrı´ guez-Puebla et al., 1998; Trigo and Palutikof, 2001). Recently, several authors have investigated the influence of the North Atlantic Oscillation (NAO) to model the winter precipitation over western Iberia (e.g. Rodo´ et al., 1997; Corte-Real et al., 1998; Trigo and Palutikof, 2001; Trigo et al. 2002). The NAO corresponds to the most important large-scale mode of atmospheric circulation in the winter season over the entire Northern Hemisphere (Hurrel, 1995). In fact this mode is present throughout the year, but it is especially prominent in winter (Barnston and Livezey, 1987), when the precipitation in western Iberia reaches its maximum seasonal value (Trigo and DaCamara, 2000). The relationship between landslide activity and low frequency atmospheric circulation oscillations has been already attempted for other areas of the world. El Ninõ events such as 1982/1983 and 1997/1998 had major impact in California and coastal Central America (Coe et al., 1998). In particular, during the 1997–1998 winter season, most of central California experienced near-record rainfall, with some areas receiving as much as 240% of the seasonal average. This heavy rainfall caused over $150 million in landslide damage in the 10-county San Francisco Bay region during the winter and spring of 1998 (Godt, 1999). The El-Ninõ 1997–1998 phenomenon had also a major impact in Kenya where the increased landslide activity was responsible for many human casualties and widespread

334


destruction of farmlands, roads, railway lines, bridges, telephone and power lines (Ngecu and Mathu, 1999). Such type of analysis has not yet been attempted over Europe. We intend to show that the influence of the NAO is extensive to the timing and frequency of major rainfall episodes and associated landslide activity near Lisbon. Moreover, the influence of this important large-scale mode of atmospheric variability on different types of landslide is also assessed. The main aims of our study are: 1. to assess the magnitude of the impact of both NAO phases on the seasonal and monthly precipitation in the Lisbon area; 2. to assess the influence of the NAO on the occurrence of landslides near Lisbon, in particular during long lasting rainfall events.

2. Data and NAO 2.1.

DATASETS

The different datasets used in this study are reported below. The analysis of the relationship between NAO and landslides is performed for the winter wet season in Portugal, comprising the months between November and March (NDJFM). Landslide dataset was obtained by detailed geomorphological mapping carried out in 5 sample areas in the Lisbon area (Figure 2). The reconstruction of past landslide activity was supported by field work, archive investigation and local interviews (Zeˆzere and Rodrigues, 2002). The most recent slope instability events (after 1978) are better documented then the older ones. Monthly values of precipitation between 1938 and 1995 were obtained from the high-resolution (0.5 latitude by 0.5 longitude) dataset developed by the Climatic Research Unit (New et al., 1999, 2000). This dataset uses,

c Figure 2. (a) Schematic geomorphological map of the area north of Lisbon. 1: front of cuesta on Cretaceous rocks; 2: front of cuesta on Tertiary rocks; 3: other cliffs; 4: river; 5: gorge; 6: terrace; 7: alluvial plain; 8: geological boundary; 9: elevation in meters; 10: sample areas; 11: location of S. Juliaõ do Tojal rain-gauge. J: Jurassic rocks (clays, marls, limestones, sandstones); C: Cretaceous rocks (sandstones, marls and limestones); B: Upper Cretaceous Volcanic Complex of Lisbon; ø: Paleogene detritic complex; M: Miocene (sandtones, limestones and clays). 1 – Pinheiro de Loures Sample Area; 2 – Lousa Sample Area; 3 – Fanho˜es Sample Area; 4 – Trancaõ Sample Area; 5 – Calhandriz Sample Area. (b) Landslide distribution in the Calhandriz Sample Area. 1: shallow translational slides; 2: translational slides; 3: rotational slides; 4: complex slope movements; 5: villages; 6: roads.


335

over Europe, a dense network of rain gauges observations (New et al., 2000). Monthly values of the NAO index between 1938 and 2001 were obtained from the Climatic Research Unit (Jones et al., 1997).

336


Precipitation records from Saõ Juliaõ do Tojal (SJT hereafter), the nearest rain gauge station to the study area (Figure 2), was provided by INAG (Portuguese Institute of Water). Daily data are available from September 1956 to August 2001 (45 complete climatological years) while monthly values cover the period between 1938 and 2001 (63 complete climatological years). The S. Juliaõ do Tojal rain gauge is representative of the Lisbon area concerning rainfall regime, as it is confirmed by the strong correlations found (R2 ‡ 0.9) between SJT and nearby rainfall stations for monthly precipitations. 2.2.

THE NORTH ATLANTIC OSCILLATION

The North Atlantic Oscillation (NAO) has been recognized more than 70 years ago as one of the major patterns of atmospheric variability in the Northern Hemisphere (Walker, 1924). However, only recently has it become the subject of a wider interest (e.g. van Loon and Rogers, 1978; Rogers, 1984; Barnston and Livezey, 1987; Hurrell, 1995; Hurrell and van Loon, 1997). Several studies have established links between the NAO phase and precipitation in western Europe and the Mediterranean basin (Hurrell 1995; Qian et al., 2000; Trigo et al., 2002). This control exerted by NAO on the precipitation regime is related to corresponding changes in the associated activity of North-Atlantic storm tracks that affect the western European border (Osborn et al., 1999; Goodess and Jones, 2002; Trigo et al., 2002). Historically, the NAO has been defined as a simple index that measures the difference of normalized surface pressure between Ponta Delgada in the Azores and Stykkisholmur in Iceland. Recently, researchers have realized that, during the winter season, stations located in Iberia could be used with some advantages over Ponta Delgada. Hurrel (1995) started to use Lisbon and Jones et al. (1997) opted for Gibraltar, nevertheless, it should be stressed that all of those indices are highly correlated, presenting correlation coefficient values higher than 0.9 between them (Jones et al., 1997; Osborn et al., 1999). Here it was decided to use the Gibraltar–Iceland index developed by the Climatic Research Unit (Jones et al., 1997). The advantages of using Gibraltar (instead of Ponta Delgada) have been comprehensively discussed in previous works (Jones et al., 1997; Osborn et al., 1999). The spatial signature of the NAO is shown in Figure 3 representing the difference of the sea level pressure between winter months (NDJFM) with a high NAO index and with a low NAO index.

3. Landslide Typology and Triggering Mechanisms Most landslides recognized in the Lisbon area are shallow movements with slip surface depth less than 10 m. Slope movements can be grouped into


337

Figure 3. Difference in sea level pressure (hPa) between winter months (NDJFM) with an NAO index >1.0 and with an NAO index 0.5 NAO 0.5 and (c) low NAO index 0.5 and (b) low NAO index < )0.5 classes. Also represented is the corresponding Gamma fit distribution.

extreme classes as well as for the entire distribution and it is particularly impressive that the percentile 90% (P90) for the high NAO index distribution is similar to the percentile 40% (P40) for low NAO index class. Both distributions reveal a non-Gaussian shape, with a small (large) tail to the right for the high (low) NAO composite. It is a well-known fact that distributions that are physically constrained to be positive, such as precipitation, are likely to be positively skewed (Wilks, 1995). Thus, a (twoparameter) gamma distribution was fitted to each composite precipitation distribution (Figures 5a and b), with the shape (a) and scale (b) parameters being estimated with the robust method of maximum likelihood. For both composites the null hypothesis (for a Kolmogorov–Smirnov test) was accepted at the 5% significance level. This is particularly important for the occurrence of natural hazards related with extreme precipitation such has


343

Table III. Percentiles (mm) of monthly rainfall in SJT for the period 1938–2001.

P10 P20 P30 P40 P50 P60 P70 P80 P90

All

NAO >0.5

21 32 48 64 77 95 121 147 196

11 19 29 34 47 56 67 79 113

NAO P90) increases from merely 1%, for the positive NAO class, to 23% for months characterized by low NAO index (Table IV). To put these results into a more general perspective we computed the spatial extent of significant impact associated with NAO on western Europe precipitation. The impact of NAO mode on the entire European continent and for the 1938–1995 period was computed using the high resolution precipitation dataset and can be visualized in Figure 6. Composites of

Table IV. Probability (%) of winter dry and wet months in SJT for the entire distribution and for the two extreme NAO classes. All Dry months

Wet months

P P90

10 20 30 30 20 10

NAO >0.5 22 40 54 9 4 1

NAO 0.5, (b) low NAO index P90) during the positive NAO class is roughly 1% and rises to 23% for the low NAO index. This non-linear behavior is well depicted by the significant changes of the associated gamma distributions, with the low NAO composite presenting a shallower and more spread distribution than the high NAO composite. The influence of the NAO mode on the precipitation field was confirmed to be extensive to most western Mediterranean basin, however it is also shown here that the spatial extent of statistically significant impact of this mode is restricted to the Iberian Peninsula, Morocco and parts of northern Mediterranean basin. In Iberia, the largest differences in magnitude can be observed over Portugal and southern Spain, indicating that sites located outside these sectors lack such a strong relationship between precipitation distribution shape and the NAO phase. We confirmed the relevance of this large-scale atmospheric circulation mode to landslide events. This was firstly assessed through the application of a 3-month moving average to both NAO index and precipitation time series. It allowed the identification of months with landslide activity as being characterized by negative average values of the NAO index and high values of average precipitation (above 100 mm/month). Using daily data we have computed the return periods associated with the entire set of landslide episodes and, based on these results, obtained a strong linear relationship between critical cumulative rainfall and the corresponding critical rainfall event duration. Rainfall statistical analysis allowed the definition of three distinct situations that trigger landslide events in the study area: 1. High intensity rainfall episodes (daily rainfall ‡130 mm; return period ‡60 years), trigger flash floods, slope movements due to bank erosion and most shallow translational slides. 2. Moderate intensity rainfall episodes (from 174 mm in 5 days to 217 mm in 15 days; return period from 2 to 13 years), are responsible for shallow translational slides and minor slides, falls and topples on the banks of the rivers. 3. Long lasting rainfall periods (from 333 mm in 30 days to 793 mm in 90 days; return period from 5 to 24 years), are responsible for the activity of deeper slope movements, such as translational slides, rotational slides and complex and composite slope movements. This is the group of landslide events mostly affected by the large-scale atmospheric circulation mode NAO. The magnitude of the NAO-precipitation relationship for Europe has been well documented in literature over the last decade (Hurrel, 1995; Rodo´

352


et al., 1997; Corte-Real et al., 1998; Trigo et al., 2002). However, only recently such connection has been used to develop precipitation forecast models and predict precipitation over Europe several months in advance (e.g. Ga´miz-Fortis et al., 2002; Rodriguez-Fonseca and Castro, 2002). Such models should be developed into an operational level with the purpose of providing important seasonal forecasting tools to be used by water resource managers and risk assessment teams. Furthermore, the use of general circulation models opens the possibility of modeling future landslide activity based on precipitation scenarios obtained under future climate change scenarios (Dehn and Buma, 1999). Acknowledgements The authors would like to thank the NCEP/NCAR for providing their reanalysis and to Ian Harris and David Viner of the Climatic Research Unit for the provision of the reanalysis data for the required window and the high-resolution precipitation data. Precipitation data for Saõ Juliaõ do Tojal was provided by INAG. The research work of Jose´ Luı´ s Zeˆzere and Maria Luı´ sa Rodrigues was supported by the European Commission through the project ‘‘Assessment of Landslide Risk and Mitigation in Mountain Areas’’ (EVG1-CT-2001-00018). We are grateful to two anonymous reviewers whose pertinent comments helped to improve the quality of this paper. This work was supported by the Portuguese Science Foundation (FCT) through project CLIVAR, contract POCTI/CTA/39607/2001, co-financed by the European Union under program FEDER. References Barnston, A. G., and Livezey, R. E.: 1987, Classification, seasonality and persistence of lowfrequency atmospheric circulation patterns, Mon. Wea. Rev. 115, 1083–1127. Caine, N.: 1980, The rainfall intensity–duration control of shallow landslides and debris flows, Geografiska Annaler 62(1–2), 23–27. Coe, J. A., Godt, J. W., and Wilson, R. C.: 1998, Distribution of debris flows in Alameda County, California triggered by 1998 El Ninõ rainstorms: a repeat of January 1982?, EOS 79(45), 266. Corominas, J.: 2001, Landslides and climate, Keynote Lectures from the 8th International Symposium on Landslides 4, 1–33. Corominas, J., and Moya, J.: 1999, Reconstructing recent landslide activity in relation to rainfall in the Llobregat River basin, Eastern Pyrenees, Spain, Geomorphology 30(1–2), 79–93. Corte-Real, J., Qian, B., and Xu, H.: 1998, Regional climate change in Portugal: Precipitation variability associated with large-scale atmospheric circulation, Int. J. Climatol. 18, 619– 635. Crozier, M.: 1986, Landslides Causes, Consequences and Environment, Croom Helm, London 252 pp.


353

Dehn, M., and Buma, J.: 1999, Modelling future landslide activity based on general circulation models, Geomorphology 30(1–2), 175–187. Dikau, R., and Schrott, L.: 1999, The temporal stability and activity of landslides in Europe with respect to climatic change (TESLEC): main objectives and results, Geomorphology 30(1–2), 1–12. Fukuoka, M.: 1980, Landslides associated with rainfall, Geotech. Eng. 11, 1–29. Ga´miz-Fortis, S., Pozo-Va´zquez, D., Esteban-Parra, M.J. and Castro-Dı´ ez, Y.: 2002, Spectral characteristics and predictability of the NAO assessed through singular spectral analysis. J. Geophys. Res., 107, 1029, doi: 10.1029/2001JD00136. Godt, J. W.: 1999, Maps showing locations of damaging landslides caused by El Ninõ rainstorms, winter season 1997–98, San Francisco Bay region, California. USGS http:// pubs.usgs.gov/mf/1999/mf-2325/. Goodess, C. M., and Jones, P. D.: 2002, Links between circulation and changes in the characteristics of Iberian rainfall, Int. J. Climatol. 22, 1593–1615. Gostelow, P.: 1991, Rainfall and landslides, In: M. Almeida-Teixeira (ed.), Prevention and Control of Landslides and Other Mass Movements, CEC, Brussels, pp. 139–161. Hurrell, J. W.: 1995, Decadal trends in the north Atlantic oscillation: regional temperatures and precipitation, Science 269, 676–679. Hurrell, J. W., and van Loon, H.: 1997, Decadal variations in climate associated with the North Atlantic Oscillation, Clim. Change 36, 301–326. Jibson, R. W.: 1989, Debris flows in Southern Puerto Rico, Geol. Soc. Am. Spec. Pub. 236, 1–13. Jones, P. D., Jonsson, T., and Wheeler, D.: 1997, Extension to the North Atlantic Oscillation using instrumental pressure observations from Gibraltar and south-west Iceland, Int. J. Climatol. 17, 1433–1450. Munõz-Dı´ az, D., and Rodrigo, F. S.: 2003, Effects of the North Atlantic Oscillation on the probability for climatic categories for local monthly rainfall in southern Spain, Int. J. Climatol. 23, 381–397. New, M. G., Hulme, M., and Jones, P. D.: 1999, Representing twentieth-century space-time climate variability. Part I: Development of a 1961–90 mean monthly terrestrial climatology, J. Clim. 12, 829–856. New, M. G., Hulme, M., and Jones, P. D.: 2000, Representing twentieth-century space–time climate variability. Part II: Development of a 1901–95 monthly grids of terrestrial surface climate, J. Clim. 13, 2217–2238. Ngecu, W. M., and Mathu, E. M.: 1999, The El-Ninõ triggered landslides and their socioeconomic impact in Kenya, Environ. Geol. 38, 277–284. Osborn, T. J., Briffa, K. R., Tett, S.F.B., Jones, P. D., and Trigo, R. M.: 1999, Evaluation of the North Atlantic Oscillation as simulated by a climate model, Clim. Dyn. 15, 685–702. Polemio, M., and Petrucci, O.: 2000, Rainfall as a landslide triggering factor: an overview of recent international research, In: E. Bromhead (ed.), Landslides in Research, Theory and Practice, Thomas Telford, London, pp. 1219–1226. Qian, B., Corte-Real, J., and Xu, H.: 2000, Is the North Atlantic Oscillation the most important atmospheric pattern for precipitation in Europe?, J. Geophys. Res. 105, 11901– 11910. Rodo´, X., Baert, E., and Comin, F. A.: 1997, Variations in seasonal rainfall in Southern Europe during the present century: Relationships with the North Atlantic Oscillation and the El Ninõ-Southern Oscillation, Clim. Dyn. 13, 275–284. Rodriguez-Puebla, C., Encinas, A. H., Nieto, S., and Garmendia, J.: 1998, Spatial and temporal patterns of annual precipitation variability over the Iberian Peninsula, Int. J. Climatol. 18, 299–316.

354


Rodriguez-Fonseca, B. and Castro, M.: 2002, On the connsection between winter anomalous precipitation in the Iberian Peninsula and North West Africa and the Summer subtropical Atlantic sea surface temperature, Geophys. Res. Lett. 29, 1863, doi: 10.1029/ 2001GL014421. Rogers, J. C.: 1984, The association between the North Atlantic Oscillation and the Southern Oscillation in the Northern Hemisphere, Mon. Wea. Rev. 112, 1999–2015. Santos, F. D., Forbes, K., and Moita, R.: 2002, Climate Change in Portugal, Scenarios, Impacts and Adaptation Measures – SIAM Project, Gradiva, Lisbon, 454 pp. Trigo, R. M., and DaCamara, C. C.: 2000, Circulation weather types and their impact on the precipitation regime in Portugal, Int. J. Climatol. 20, 1559–1581. Trigo, R. M., and Palutikof, J. P.: 2001, Precipitation scenarios over Iberia: a comparison between direct GCM output and different downscaling techniques, J. Clim. 14, 4422–4446. Trigo, R. M., Osborn, T. J., and Corte-Real, J.: 2002, The North Atlantic Oscillation influence on Europe: climate impacts and associated physical mechanisms, Clim. Res. 20, 9–17. Trigo, R.M., Pozo-Vazquez, D., Osborn, T.J, Castro-Diez, Y., Ga´mis-Fortis, S. and EstebanParra, M.J.: 2004, North Atlantic Oscillation influence on precipitation, river flow and water resources in the Iberian Peninsula. Int. J. Climatol., 24, 925–944. Van Asch, T., Buma, J., and VanBeek, L.: 1999, A view on some hydrological triggering systems in landslides, Geomorphology 30(1–2), 25–32. Van Loon, H., and Rogers, J. C.: 1978, The seesaw in winter temperatures between Greenland and Northern Europe, Part 1: General description, Mon. Wea. Rev. 106, 296–310. Walker, G. T.: 1924, Correlations in seasonal variations of weather, IX, Mem. Ind. Meteorol. Dept. 24, 275–332. Wieczorek, G. F.: 1996, Landslides triggering mechanisms, In: Turner (ed.), Landslides: Investigation and Mitigation, National Research Council, Transportation Research Board, Washington, pp. 76–90. Wilks, D. 1995, Statistical Methods in the Atmospheric Sciences: An Introduction, International Geophysical Series 464, Academic Press, 59 pp. Zeˆzere, J. L.: 2000, Rainfall triggering of landslides in the Area North of Lisbon, In: E. Bromhead (ed.), Landslides in Research, Theory and Practice, Thomas Telford, London, pp. 1629–1634. Zeˆzere, J. L., Ferreira, A. B., and Rodrigues, M. L.: 1999a, The role of conditioning and triggering factors in the occurrence of landslides: A case study in the area north of Lisbon (Portugal), Geomorphology 30(1–2), 133–146. Zeˆzere, J. L., Ferreira, A. B., and Rodrigues, M. L.: 1999b, Landslides in the north of Lisbon Region (Portugal): Conditioning and triggering factors, Phys. Chem. Earth (Part A) 24(10), 925–934. Zeˆzere, J. L., and Rodrigues, M. L.: 2002, Rainfall thresholds for landsliding in Lisbon Area (Portugal), In: Rybar (ed.), Landslides, A.A. Balkema, Lisse, pp. 333–338.