The Influence of the North Atlantic Oscillation on Rainfall Triggering of Landslides near Lisbon

Transcription

1 Natural Hazards (2005) 36: Ó Springer 2005 DOI /s The Influence of the North Atlantic Oscillation on Rainfall Triggering of Landslides near Lisbon RICARDO M. TRIGO 1,2,w, JOSÉ L. ZEˆ ZERE 3, MARIA L. RODRIGUES 3 and ISABEL F. TRIGO 4 1 Centro de Geofıśica da Universidade de Lisboa, Departamento de Fıśica, Faculdade de Cieˆncias, Universidade de Lisboa, Campo Grande, Ed C8, Piso 6, , Lisboa, Portugal; 2 Departamento de Engenharias, Universidade Luso fona, Lisboa, Portugal; 3 Centro de Estudos Geogra ficos, Universidade de Lisboa, Portugal; 4 Instituto 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 Cieˆ ncias, Universidade de Lisboa, Campo Grande, Ed C8, Piso 6, , Lisboa, Portugal. Tel: , Fax: , rtrigo@fc.ul.pt.

2 332 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; Zêzere, 2000; Corominas, 2001, Zeˆ zere and Rodrigues, 2002). In previous works we have discussed and analyzed the triggering factors of landslides in the area North of Lisbon (Zeˆ zere et al., 1999a, b; Zeˆ zere, 2000; Zêzere 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

3 NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES 333 Figure 1. Annual precipitation (climatological year) distribution at S. Julião 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 (Rodríguez-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 Nin o 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 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-Nin o phenomenon had also a major impact in Kenya where the increased landslide activity was responsible for many human casualties and widespread

4 334 RICARDO M. TRIGO ET AL. 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 (Zêzere 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, 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. Julia o 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 Trancão 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. c

5 NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES 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).

6 336 RICARDO M. TRIGO ET AL. Precipitation records from São Julia o 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. Julia o do Tojal rain gauge is representative of the Lisbon area concerning rainfall regime, as it is confirmed by the strong correlations found (R 2 0.9) between SJT and nearby rainfall stations for monthly precipitations 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

7 NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES 337 Figure 3. Difference in sea level pressure (hpa) between winter months (NDJFM) with an NAO index >1.0 and with an NAO index <)1.0 from 1958 to five main categories, according to the type of movement, affected material and triggering mechanism (Table I): (a) Shallow translational slides which affect mostly slope deposits along planar slip surfaces generally located at the contact with the underlying impermeable bedrock. Although very numerous (36% of the total landslides), they have small dimension (mean area 552 m 2 ; mean volume 286 m 3 ). (b) Translational slides which also move along planar slip surfaces, but always affect the bedrock. Most of these landslides are structurally controlled: they occur on cataclinal slopes formed by an alternation of layers of different permeability and shear strength; the rupture zone develops along the impermeable bedding planes. These landslides are larger and deeper than the shallow ones (mean area 4,059 m 2 ; mean volume 5,232 m 3 ). (c) Rotational slides which show a typical concave slip surface and develop on the most homogeneous and isotropic lithologies (clays and marls). Although less numerous than the previous types (7% of the total slope movements), these are larger than the translational slides (mean area 9,407 m 2 ; mean volume 34,843 m 3 ). (d) Complex and composite slope movements present at least two different types of mechanisms. The most important examples combine translational and rotational movements or slide and flow mechanisms. Some of the larger landslides recently occurred in the Lisbon area are included in this category (mean area 27,011 m 2 ; mean volume 42,998 m 3 ).

8 338 RICARDO M. TRIGO ET AL. Table I. Main slope movements and basic morphometrical parameters of the landslides identified in 5 sample areas (62 km 2 ) in the Lisbon area. Type of movements Number of cases % of total landslide Slope angle ( ) Depth (m) Area per landslide (m 2 ) Estimated volume per landslide (m 3 ) Mean Std. dev. Mean Std. dev. Mean Std. dev. Mean Std. dev. Shallow translational slide Translational slide ,059 5,470 5,232 8,822 Rotational slide ,407 21,664 34, ,434 Complex and composite movements ,747 27,011 42, ,801 Slides and falls due to bank erosion ,588 Total landslides ,889 14,389 7,702 64,676 Landslide volume was estimated from the 2-D reconstruction of individual slope movements.

9 NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES 339 (e) Slope movements triggered by bank erosion, amount to almost 40% of the total landslides. Included in this class are fall, topple and slide movements which are confined to river banks and have modest dimensions (mean area 397 m 2 ; mean volume 595 m 3 ). As this landslide category is mainly relative to river bank instability, it will not be included in the following analysis. Landslides in the Lisbon area are induced by rainfall. Nevertheless, the previously mentioned types of slope movements are related with distinct hydrological conditions. Shallow translational slides are usually triggered by the water infiltration in unconsolidated slope deposits, which cover impermeable rocks. The soil saturation is responsible for the reduction of the shear strength of the soil, by the temporary rise in pore water pressure, and by the loss of the soil apparent cohesion (Gostelow, 1991; Wieczorek, 1996). In this case slope deposits became unstable, mostly on slopes with high topographic gradient (Table I), and along artificial cuts. These landslides are mostly activated in the study area by intense and concentrated rainfall 1 15 days long (Zêzere, 2000; Zeˆ zere and Rodrigues, 2002). Translational slides, rotational slides and complex and composite slope movements are triggered by groundwater level rising and the shear strength reduction (Gostelow, 1991; Van Asch et al., 1999). Such hydrological conditions occur as a consequence of rainfall periods days long in Lisbon area (Zêzere, 2000). Slope movements due to bank erosion are mostly activated during flash flood episodes (November 1967 and November 1983). These situations are related with very intense and concentrated rainfall inducing vigorous erosion along river banks (Zêzere, 2000; Zêzere and Rodrigues, 2002). 4. The Impact of NAO on Regional and Local Precipitation Recent studies have evaluated the correlation between the NAO index and the precipitation over Iberia (e.g. Hurrell 1995; Rodo et al., 1997; Rodrı - guez-puebla et al., 1998) and, in particular, over Portugal (Corte-Real et al., 1998). The extent of NAO influence in western Iberian Peninsula precipitation in winter is sufficiently strong to impact in the terrestrial branch of the hydrological cycle. In fact, the winter NAO mode is responsible for roughly 60% of the corresponding seasonal flow variability of the three largest international Iberian (Portugal and Spain) rivers, namely the Douro in the north, the Tagus (centre) and the southern Guadiana (Trigo et al., 2004). Here we try to extend these studies and show how the different phases of the NAO change the probability of occurrence of wet and dry winter months/season. This will be attempted through a more comprehensive characterization of the relationship between the NAO index and the corresponding precipitation in S. Julia o do Tojal (SJT).

10 340 RICARDO M. TRIGO ET AL. Since the original NAO index is not normalized, we decided to normalize the entire winter NAO index so it has zero mean and standard deviation one. Then, we defined the seasonal high NAO composite (low NAO composite) to be constituted by all winters (average of NDJFM values) with NAO index higher than 0.5 (lower than )0.5). Between 1939 and 2001 (63 winters), the number of winter seasons with high NAO index (20) is similar to the number characterized by a low NAO index (19). The remaining winters (24) are characterized by near normal values of the NAO index. Table II summarizes the main statistical characteristics of the precipitation distribution associated with both extreme composites. It is worth noticing that the winter mean precipitation almost doubles between the high NAO (339 mm) and the low NAO (619 mm) composites. Correlation coefficients between winter precipitation (NDJFM) and contemporaneous winter NAO index were computed and the inter-annual variability of winter precipitation vs. winter NAO index can be observed in Figure 4a. Both curves were normalized and the NAO index multiplied by )1 to facilitate visual comparisons. The correlation coefficient between the original curves is R=)0.69 (statistically significant at the 1% level). The histogram of winter precipitation for the high (low) NAO composites is represented in Figure 4b (Figure 4c). A simple comparison of these two histograms puts into evidence an important increase of precipitation values between the high and the low NAO composites. However, both histograms and Table II results further indicate that this increment is not represented by a simple shift to the right of the entire distribution. In fact, both spread measures, the standard deviation and the inter-quartile range (IQR), also present an increase, i.e. the precipitation distribution associated to the low NAO index composite encompasses a wider range of values than the corresponding high NAO index composite. Following a recent study (Mun oz-diaz and Rodrigo, 2003) we decided to analyze in more detail the characteristics of precipitation distributions associated to high and low NAO indices composites. Rainfall composites were obtained on a monthly basis containing only those individual months Table II. Main statistical characteristics of the winter (NDJFM) precipitation distribution (mm) at SJT ( ) associated with the entire distribution and both extreme NAO indices composites. Mean Std IQR Skewness All NAO > NAO <) Std: standard deviation, IQR: inter-quartile range.

11 NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES 341 Figure 4. (a) Interannual variability of the mean winter (NDJFM) precipitation (solid curve), for SJT and the corresponding winter NAO index (multiplied by )1 to facilitate analysis, dashed curve); both curves have been normalized and so are dimensionless. Histograms of winter (NDJFM) precipitation at SJT for (b) high NAO index >0.5 and (c) low NAO index <)0.5 classes. with the monthly NAO normalized index higher than 0.5 (high NAO composite) and those with the NAO index lower than )0.5 (low NAO composite). The number of months with high NAO index is slightly lower (99) than those characterized by a low NAO index (102). The remaining months (117) are characterized by near normal values of the NAO index. The histogram of monthly precipitation for the high (low) NAO composites is represented in Figure 5a (Figure 5b) and includes all individual monthly values between November and March. The change from lower to higher precipitation classes, when the NAO index changes from positive to negative phase, is striking. Table III presents the percentiles for both

12 342 RICARDO M. TRIGO ET AL. Figure 5. Histograms of monthly precipitation at SJT for (a) high 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% (P 90 ) for the high NAO index distribution is similar to the percentile 40% (P 40 ) 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

13 NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES 343 Table III. Percentiles (mm) of monthly rainfall in SJT for the period All NAO >0.5 NAO <)0.5 P P P P P P P P P landslides and floods. Table IV presents the probability of dry and wet months as computed from the Gamma distributions fitted to both high and low NAO composites. The threshold values chosen to characterize dry months are the percentiles P 10 (21 mm), P 20 (32 mm) and P 30 (48 mm), while those that characterize wet months are P 70 (121 mm), P 80 (147 mm) and P 90 (196 mm), all obtained from the entire distribution percentiles (see 2nd column in Table III). It is immediately striking that the probability of a very dry month (P <P 10 ) during the negative phase of the NAO is virtually null (1%) while it reaches 22% for the positive phase. In contrast, the probability of a very wet month (P >P 90 ) 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 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 NAO >0.5 NAO <)0.5 Dry months P<P P<P P<P Wet months P >P P >P P >P

14 344 RICARDO M. TRIGO ET AL. winter months characterized by high and low NAO index are shown in Figure 6a and b, respectively; the high magnitude of the anomaly field over western Iberia is evident. The differences between high and low NAO composites observed precipitation are represented in Figure 6c, but only if those differences are significant at the 1% level (statistical significance of composites was computed with a two-tailed t-test; null hypothesis of equal means). Over northern Europe there are relatively small, scattered patches of significant positive differences, mainly over the UK, Scandinavia and (a) (b) (c) Figure 6. Precipitation anomaly fields (mm/month) for winter months with (a) high NAO index >0.5, (b) low NAO index <)0.5 and (c) their difference (represented only if significant at the 5% level).

15 NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES 345 Benelux (Trigo et al., 2004). On the contrary, southern Europe shows widespread, statistically significant negative differences in a region stretching from Western Iberia to the Black Sea. In particular, the entire Iberian Peninsula exhibits significant differences between composites and the highest values in magnitude can be observed over Portugal and southern Spain. Thus, it is not expected that most sites located within the Mediterranean basin present such a strong relationship between the shape of the precipitation distribution and the NAO phase as described here for Portugal, and in Mun oz-diaz and Rodrigo (2003) for southern Spain. 5. The Impact of NAO on Landslide Activity In the previous section we have shown that the phase of the NAO circulation mode is of paramount importance to model the temporal precipitation distribution on Lisbon region. However, it is not straightforward to correlate the NAO index with the landslide activity, and this is partly due to both the small number of landslide episodes and the low number of recognized slope movements for some of these episodes. Table V shows that for the 45-year period that spans between 1956 and 2001, only 19 landslide triggering events were reported in the study area. Table V summarizes the most relevant features of each event, including date, predominant type of landslide and critical rainfall amount/duration. Two different approaches will be used to prove the relevance of the NAO on the timing, frequency and magnitude of landslide events RUNNING AVERAGES We applied a 3-month moving average to filter the original NAO index (non-normalized) and SJT precipitation time series, restricting the analysis to monthly data from the wet season (NDJFM). October values are used to compute November and December averages. September values are excluded because the NAO-precipitation relationship for summer months is weak (Mun oz-diaz and Rodrigo, 2003). Thus, values for February of year n, correspond to the rainfall and NAO index averages computed between December of year n)1 and February of year n, while values for November of any year are restricted to the October and November values for that same year. For landslides episodes that have occurred in the first 5 days of the month we only considered the NAO index and precipitation values from the previous two months (episodes 5, 12 and 17). Figure 7 represents the scatter plot between both filtered time series, and it is limited to the months between November and March. Small black diamonds represent months where landslide episodes did not occur (or were not reported), gray circles correspond to months with only one recognized landslide

16 346 RICARDO M. TRIGO ET AL. Table V. Temporal occurrence of rainfall triggered landslides in the Lisbon area from 1956 to Episode Date (yy/mm/dd) Critical rainfall amount/duration mm (dd) Return period (years) Landslide typology /12/ (10) 2.5 a /03/ (10) 4 a /11/ (1) 60 a /11/ (30) 6.5 b /03/ (15) 3.5 a, d /02/ (75) 20 b, c, d /12/ (5) 13 a /11/ (1) 200 a /02/25 52 (1) 2 a /11/ (15) 2 a /11/ (15) 4.5 a /12/ (30) 5.5 b, c, d /12/ (40) 20 b, c, d /01/ (60) 10 b, c, d /01/ (75) 18 b, c, d /01/ (40) 20 b, c, d /02/ (90) 24 b, c, d /01/ (60) 5 c /01/ (60) 5.5 c Landslide typology: a shallow translational slides; b translational slides; c rotational slides; d complex and composite slope movements. episode (e.g. February 1979, episode 6), while gray squares represent those months with more than one slope instability episode (e.g. January 1996, episodes 14, 15 and 16). Numbers close to gray symbols represent the landslide episode number reported in Table V. Important remarks can be made by analyzing Figure 7: 1. The linear correlation between time series is )0.63, a value statistically significant at the 1% level. Nevertheless, the relationship between these mean time series is better represented by a non-linear 2nd degree polynomial curve. This is in accordance with results obtained in the previous section, namely the non-stationary shape of the precipitation distribution moving from high to low NAO index classes (Figures 5 and 6). 2. Most of the months where landslide events were reported are above the polynomial fitting curve (or very close to it), and above the horizontal line indicating the threshold of 101 mm/month (mean value of 3-month

17 NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES Month averages (November-March) Average precipitation NAO index Figure 7. Scatter plot between 3-month moving average NAO index and SJT precipitation time series (November March), for the period between 1956 and 2001 (225 months). Small black diamonds: months with no landslide episodes reported. Gray circles: months with one recognized landslide episode. Gray squares: months with more than one recognized landslide episode. Numbers next to gray symbols represent the landslide episode number as given in Table V. average precipitation). Moreover, all cases are characterized by negative values of the averaged NAO index. 3. Six of the landslide episodes (12 17 that have occurred in 1989 and 1996, Table V) are characterized by extremely negative average values of the NAO index (lower than )1) and extremely high values of average precipitation (clearly above 200 mm/month). 4. It is worth mentioning that most of the landslide episodes located next to the polynomial curve (episodes 1, 2, 5, 9, 10 and 11) are characterized by the lowest critical return period values (<5 years, Table V) ACCUMULATED CRITICAL RAINFALL The second approach analyzes the relationship between rainfall and landslide occurrence and is carried out using 45 years ( ) of daily precipitation at SJT. The methodology is based on previous work carried out by the authors (Zeˆ zere et al., 1999a, b; Zeˆ zere, 2000; Zeˆ zere and Rodrigues, 2002) and includes the calculation of accumulated rain for 1, 5, 10, 15, 30, 45, 60, 75 and 90 consecutive days before the periods of major slope instability, observed in the last four decades. A Gumbel law is applied to compute the return period of the obtained rainfall intensity/

18 348 RICARDO M. TRIGO ET AL. duration combinations. Critical pairs of rainfall amount duration were defined assuming as critical values the combinations with the higher return period. Results obtained are summarized in Table VI and critical rainfall amount durations values are highlighted in bold. Although different critical durations are found for different episodes, we must stress the importance of intense rainy days occurrence at the end of rainfall periods for most of the considered episodes, as it is confirmed by the cumulative rainfall for shorter durations (1 and 5 days, Table VI). The relevance of very intense rainfall on slope instability is particularly evident for the episodes of 1967 (episode 3) and 1983 (episode 8). On November 25, 1967, from 7 p.m. to midnight, 111 mm was registered in SJT, and 79 landslides were reported in the study area, mostly of shallow type. On November 18, 1983, heavy rain lasted over a 12-h interval, but rainfall was particularly high for a few minutes. At Lisboa/Portela (10 km south to SJT), the following data have been recorded: 5 min, 14 mm; 10 min, 17 mm; 20 min, 24 mm. One hundred and seventy eight landslides were triggered within the study area, namely shallow translational slides and soil falls and slides due to bank erosion. Naturally, these intensive rainfall episodes with short critical durations present a very tenuous association ship with the NAO. If we consider the 19 reported landslide events it is possible to observe a strong correlation between cumulative rainfall and rainfall event duration (large diamonds in Figure 8, numbers in accordance with Table V). The regression line plotted in this figure is given by the simple equation: C r = 7.4D+107 where C r is the cumulative rainfall in mm, and D is the duration in days. In order to put into a broader context the magnitude of these critical rainfall amount duration values on the activity of landslides we have considered the approach developed by Corominas and Moya (1999), originally applied to the Llobregat region (eastern Pyrenees). For every year where landslide episodes were not recognized (34 years) we selected the yearly maximum rainfall value for consecutive durations of 1,5, 10, 15, 30, 40, 60, 75 and 90 days. The obtained 306 cases were also plotted in Figure 8 (small dots). For long duration intervals almost all critical rainfall amount duration values associated with landslide episodes are higher than corresponding maximum values obtained on the remaining 34 years (episodes identified as Group 1 in Figure 8). Landslide events that were triggered by these long lasting rainfall periods are mainly of types b, c and d, i.e. rotational and translational slides, as well as complex and composite slope movements, those types that are characterized by few occurrences but with larger volumes per landslide (Table I). As far as concerns shorter durations (Group 2 in Figure 8) triggered landslides are mostly of shallow translational slide type (type a), characterized by many occurrences with small volumes (Table I). For medium length rainfall periods (10 and 15 days) there are

19 NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES 349 Table VI. Cumulative rainfall from 1 to 90 antecedent days and corresponding return periods for 19 landslide episodes (rainfall data from S. Julia o do Tojal, period: ; R rainfall (mm); R.P. return period (years)). 1 day 5 days 10 days 15 days 30 days 40 days 60 days 75 days 90 days 1958 R (mm) Dec.19 R.P. (y) R (mm) Mar. 9 R.P. (y) R (mm) Nov. 25 R.P. (y) R (mm) Nov. 15 R.P. (y) R (mm) Mar. 4 R.P. (y) R (mm) Feb. 10 R.P. (y) R (mm) Dec. 30 R.P. (y) R (mm) Nov. 18 R.P. (y) R (mm) Feb. 25 R.P. (y) R (mm) Nov. 22 R.P. (y) R (mm) Nov. 25 R.P. (y) R (mm) Dec. 5 R.P. (y) R (mm) Dec. 21 R.P. (y) R (mm) Jan. 9 R.P. (y) R (mm) Jan. 23 R.P. (y) R (mm) Jan. 28 R.P. (y) R (mm) Feb. 1 R.P. (y) R (mm) Jan. 6 R.P. (y) R (mm) Jan. 9 R.P. (y)

20 350 RICARDO M. TRIGO ET AL. R (mm) Cr = D R 2 = \\ consecutive days Figure 8. Regression line between critical cumulative rainfall amount and corresponding rainfall duration. Black diamonds: values associated to each one of the 19 identified landslide episodes. Small dots: values obtained from the yearly maximum cumulative rainfall values for all duration intervals (computed for years without reported landsides). important events without landslide activity. These cases might be related with the small size of landslides that can be produced in those situations, being difficult to identify in the fieldwork or find in archive documentation. 6. Discussion and Conclusions We evaluated the magnitude of the NAO influence on both monthly and seasonal precipitation for an area prone to landslides, located north of Lisbon. Previous works have shown that when the NAO is in its negative phase shifts southwards many low pressure systems from the west that usually cross the British Islands, thus inducing above normal precipitation in western Iberia (Corte-Real et al., 1998; Trigo et al., 2002). Results obtained here prove that the large inter-annual variability of winter precipitation in the region is largely modulated by the NAO mode. Thus, while it is expected that the low NAO presents higher average precipitation values than the corresponding high NAO class, the magnitude of differences obtained here is impressive and statistically significant. In fact the high NAO index composite presents a considerable lower median value (47 mm/ month) than the corresponding low NAO index class (134 mm/month). Furthermore, the entire precipitation distribution associated with the low NAO index composite presents higher values of standard deviation and inter-quartile range than the corresponding high NAO index composite. The application of three distinct gamma distributions to fit the entire precipitation series and both extreme classes has allowed the computation

21 NORTH ATLANTIC OSCILLATION ON RAINFALL TRIGGERING OF LANDSLIDES 351 of probabilities associated with dry and wet years for high and low classes. The probability of a wet month (P >P 70 ) is just 9% for the positive NAO class and jumps to 55% for months characterized by low NAO index. Moreover, the probability of occurrence of a very wet month (P >P 90 ) 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

22 352 RICARDO M. TRIGO ET AL. 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. Gámiz-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 São Julia o do Tojal was provided by INAG. The research work of Jose Luís Zeˆ zere 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 ). 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, Caine, N.: 1980, The rainfall intensity duration control of shallow landslides and debris flows, Geografiska Annaler 62(1 2), Coe, J. A., Godt, J. W., and Wilson, R. C.: 1998, Distribution of debris flows in Alameda County, California triggered by 1998 El Nin o 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, 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), 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, Crozier, M.: 1986, Landslides Causes, Consequences and Environment, Croom Helm, London 252 pp.

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