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The Lisbon Metropolitan Area (LMA) is a Portuguese administrative region that encompasses 18 municipalities and is characterized by a high risk of landsliding as a consequence of the high susceptibility and exposure of people and assets. Therefore, this work aimed to assess the evolution of exposure levels to landsliding in each municipality of LMA, based on three timesteps (1995, 2011 and 2018), through the following summarized steps: 1) Landslide susceptibility modelling for LMA using an inventory that contains 1268 landslides, mostly rotational and translational slides; 2) Assessment of the relationship between the number of existing buildings and the urban area represented in the official land use map in 2011; 3) Assessment of the population exposed to landslides in 2011 (using Census data) within and outside the urban areas, based on the dasymetric distribution approach; 4) Backward and forward projection of population exposure to landsliding in the years of 1995 and 2018, for which official land use maps are available. Projections are based on relations found in 2) and 3). Overall, the results show that population exposure to landsliding has increased over time in LMA, but differently depending on the municipality considered. The relations found allow to estimate future population and assets exposure based on scenarios of urban expansion and population growth.S.C. Oliveira was financed by the Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017). Ricardo A. C. Garcia was financed by the project Riskcoast - Development of tools to prevent and manage geological risks on the coast linked to climate change (SOE3/P4/EO868, Interreg Sudoe). CEG Research Unit UID/GEO/00295/2019.

On November 18, 1983, a heavy and short-duration rainfall event (164 mm/24 hours ) occurred, particularly, in the northern Lisbon metropolitan area, associated with an atmospheric depression, localised southwest of the Azores islands, which evolved into a cut-off depression. This rainfall event triggered a large number of landslides, with evident regional importance, which remained little explored until now.Event-based landslide inventories identify the location of landslides caused by a single trigger, which is crucial to understand the predisposition factors and to assess landslide susceptibility for different types of landsides occurring in the study area. The purpose of this study is to elaborate a complete event-based landslide inventory for this rainfall-triggered event, which is used to model landslide susceptibility at the regional scale, helping to understand the combined action of the trigger and the landslide predisposing factors.The landslide inventory is built based on the interpretation of high-resolution analogic aerial photographs (1:15,000 scale), obtained after the event, between the 8th and the 13th December 1983. Recent orthophotomaps covering the complete study area were used as the baseline image for the process of geo-referencing the aerial photos. In addition, it was necessary to perform local correction of deformations, resulting from the conic projections of aerial photographs, to improve the geometry of landslide limits. The landslide inventory allows to identify the landslide event spatial extension as well as to characterize the landslide typology, landslide morphometric parameters and event magnitude.The landslide susceptibility is assessed using the event-based landslide inventory and a set of geo-environmental landslide predisposing factors that are modelled with the statistical method of the Information Value. The obtained results are interpreted taking in account the combination of landslide predisposing factors, but also of the spatial distribution of the rainfall that triggered the landslide event. AcknowledgementsThis work is part of the project Riskcoast - Development of tools to prevent and manage geological risks on the coast linked to climate change (SOE3/P4/EO868, Interreg Sudoe). Sérgio C. Oliveira is funded by FCT - Portuguese Foundation for Science and Technology, I.P., through the project BeSafeSlide - Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017) and by the Research Unit UIDB/00295/2020, UIDP/00295/2020.

This work aims to assess the physical vulnerability of buildings (PVB) exposed to landslides that can be triggered by rainfall and earthquake in the Lisbon metropolitan area (LMA).Susceptibility to landslides triggered by rainfall was assessed with a statistical model (Information Value), using seven predisposing factors: slope, aspect, geology, curvature, land use, and topographic wetness and position (TPI) indexes. A landslide inventory containing 4k landslides identified in the Lisbon and Tagus Valley region was used. The ROC curve of this model produced an AUC of 0.92. In this approach, the area defined as most susceptible was selected to assess the PVB for each exposed building to landslides with a slip surface depth of 1 m and an accumulated material height of 0.5 m.Susceptibility to landslides triggered by earthquakes was assessed with an Analytic Hierarchy Process to achieve the relative weights based on the Saaty’s scale of influence using six predisposing factors: slope, curvature, TPI, geology, PGA and distance to faults. The results of this model were compared with a historical inventory of landslides triggered by earthquakes in the LMA obtained from documental sources (Vaz and Zêzere, 2016). The area most susceptible to landslides (8th and 9th deciles) was selected to assess the PVB for each exposed building to landslides with a slip surface depth of 3 m and an accumulated material height of 1 m.In both cases, the PVB assessment considered all exposed buildings with a residential function surveyed in the 2011 Census (Georeferenced Buildings Database - BGE), including the following parameters: construction material, presence of reinforced structure, number of floors, conservation status, and need for repairs in the structure and finishes. Each parameter was divided into a set of building classes obtained from BGE. A score was given to each building class and the respective parameter. Both scores and parameters’ weights are based on expert opinion and dedicated literature (Guillard-Gonçalves et al., 2016; Pereira et al., 2020).The analysis allowed us to observe meaningful regional multi-hazard potential interactions between earthquake and rainfall triggered landslides which can generate, in space and time, a complex level of damages scenarios for residential buildings. Additionally, it contributes to identifying risk hotspots and possible risk adaptation and mitigation measures. AcknowledgmentsTo MIT-RSC - Multi-risk Interactions Towards Resilient and Sustainable Cities (MIT-EXPL/CS/0018/2019), Portuguese Foundation for Science and Technology (FCT I.P.), MIT-Portugal program; RISKCOAST (SOE3/P4/EO868), Interreg-SUDOE program. Pedro Pinto Santos financed through FCT I.P contract CEECIND/00268/2017. CEG Research Unit UID/GEO/00295/2019 References:Guillard-Gonçalves, C., Zêzere, J. L., Pereira, S., & Garcia, R. A. (2016). Assessment of physical vulnerability of buildings and analysis of landslide risk at the municipal scale: application to the Loures municipality, Portugal. Natural Hazards and Earth System Sciences, 16(2), 311-331.Pereira, S., Santos, P. P., Zêzere, J. L., Tavares, A. O., Garcia, R. A. C., & Oliveira, S. C. (2020). A landslide risk index for municipal land use planning in Portugal. Science of the Total Environment, 735, 139463.Vaz, T., & Zêzere, J. L. (2016). Landslides and other geomorphologic and hydrologic effects induced by earthquakes in Portugal. Natural Hazards, 81(1), 71–98.

From the territorial land use planning perspective new urban areas have been safeguarded during the last decades by the Portuguese regulation and practice that consider land use restrictions on landslide hazard prone areas. Nevertheless, the fatalities due to landslides did not reduce in number, mostly due to the occurrence of rapid shallow landslides, affecting people inside buildings and more recently, inside vehicles, as a consequence of the increasing people’s mobility. When the exposure is to deep-seated landslides, structures and infrastructures frequently undergo intense destruction, leading to severe disruption of economic and social activities.The present work intends to evaluate actual exposure of road network to landslides in the context of a Landslide Early Warning System (LEWS) prototype, developed upon both soft and low-cost technology. To achieve this main goal, three specific objectives were defined: (i) to evaluate the road network exposure considering different road types; (ii) to evaluate the re-routing of circulation for users, the loss of access to functions or services, due to travel time increase; and (iii) to evaluate the road network connectivity hierarchy and its contribute to define landslide risk hotspots for evacuation and rescue access in case of disastrous landslide events.The study area corresponds to four municipalities (Alenquer, Arruda dos Vinhos, Sobral de Monte Agraço and Vila Franca de Xira) that are partially included in the Grande da Pipa River (GPR) basin, which is one of the most landslide prone areas in Portugal. The road network hierarchy data is based on available official road network maps; the road segment connectivity role in regional network is based on graph analysis; and the landslide risk hotspots - are classified by combining the different types of road segments, the re-routing travel time scenarios and the road network hierarchy and network connectivity function with the shallow and deep-seated landslide maps produced in the context of the BeSafeSlide project.The final results will be incorporated on the LEWS prototype, which is conceptualized to be people-centred, which means, not specifically focused on hazardous processes, but on reducing exposure and vulnerability, allowing an effective implementation of risk management strategies, contributing for increasing resilience and adaptive capacity building of local communities.Acknowledgments: This work was financed by national funds through FCT (Foundation for Science and Technology, I. P.), in the framework of the project BeSafeSlide – Landslide early warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017), and the Research Unit UIDB/00295/2020 and UIDP/00295/2020.

As observed worldwide during the last decades, landslides are one of the deadliest natural hazards in mainland Portugal and Azores archipelago being responsible for significant direct and indirect societal and economic losses, justifying the implementation of a landslide early warning system at the regional scale.The BeSafeSlide project aims to develop and implement a soft technology/low-cost prototype for precipitation-triggered landslide early warning system (LEWS) in Portugal. We plan it to allow be adaptable to a changing climate and a changing land use by working with different climate scenarios. Future changes on regional rainfall patterns due to climate change were evaluated in the LEWS for 2071-2100 period, considering two emission scenarios: RCP 4.5 and RCP 8.5. To evaluate future exposure trends and effects in risk analysis, simulations of changes in land use, by the end of the 21th century, will be carried out. The uncertainty of future projections will be addressed by developing a set of different scenarios.The LEWS prototype for Portugal is sustained on different types of regional rainfall thresholds for landslide occurrence based on daily/hourly rainfall series available for each BeSafeSlide study area. The proposed prototype aims at integrating 3-day rainfall forecasts on rainfall thresholds monitoring and on dynamic physically based susceptibility models, to anticipate changes in hydrological conditions and consequently on the spatio-temporal occurrence of landslides. Special attention is given to two different types of rainfall-triggered landslide events, recognized as responsible for shallow and deep-seated landslides occurrence on natural slopes, which are permanently monitored within the regional early warning system in hotspot risk areas: (i) landslide events associated to intense, short-duration rainfall periods; and (ii) landslide events associated to long-lasting rainfall periods.The LEWS main goals are to provide information to civil protection services to anticipate and manage people’s evacuation from landslide prone areas and to ensure the maintenance and operability of regional transport, energy and communications networks and the safeguarding of people´s lives. Although the LEWS is being developed within the framework of Portugal we expect to be applicable in different settings. The application of the LEWS will define warning communication procedures, assess response capacity of stakeholders and develop social capacity practices, to reduce vulnerability and mitigate risk, providing a reduction of affected people, economic losses and critical infrastructures/basic services disruptions.Acknowledgments: This work was financed by national funds through FCT (Foundation for Science and Technology, I. P.), in the framework of the project BeSafeSlide – Landslide early warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017), and the Research Unit UIDB/00295/2020 and UIDP/00295/2020.

It is expected that landslide events will occur more frequently, throughout the century, as a direct consequence of climate change. The main triggering factor, over Portugal mainland, is extreme precipitation. Thus, the aim of this study relied on the assessment of the projected future changes in the extreme precipitation over Portugal mainland and quantifying the correlation between extreme rainfall events and landslide events through Rainfall Triggering Thresholds (RTT). This methodology was applied for two specific locations within two Portuguese areas of great geomorphological interest.To evaluate the possible projected changes in the extreme precipitation, we used the Iberia02 dataset and the EURO-CORDEX models’ runs at a 0.11º spatial resolution. First, it was analyzed the models’ performance to simulate extreme values in the precipitation series. The simulated precipitation relied on RCM-GCM models’ runs, from EURO-CORDEX, and a Multimodel ensemble mean. The extreme precipitation assessment relied on the values associated to the highest percentiles, and to the values associated to the RTTs’ percentiles. To evaluate the possible future changes of the precipitation series, both at the most representative percentiles and RTTs’ percentiles, a comparison was made between the simulated values from EURO-CORDEX historical runs (1971-2000) and the simulated values from EURO-CORDEX future runs (2071-2100), considering two emission scenarios: RCP 4.5 and RCP 8.5. In the models’ performance, the Multimodel ensemble mean appeared to be within the best representing models. As for the projected changes in the extreme precipitation for the end of the century, when following the RCP 4.5 scenario, most models projected an increase in the extreme values, whereas, when following the RCP 8.5 scenario, most models projected a decrease in the extreme values. AcknowledgementsThis work was financed by national funds through FCT–Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide (PTDC/GES-AMB/30052/2017)

Landsliding is a complex phenomenon and its modelling aimed at predicting where the processes are most likely to occur is a tricky issue to be performed. Apart the chosen modelling approach, for both data-driven and physically-based models, paying adequate attention to the predisposing and triggering factors, as well as the input parameters is no less important. Generally, shallow landslides mobilize relatively small volumes of material sliding along a nearly planar rupture surface which is assumed to be roughly parallel to the ground surface. In the literature it is also widely accepted that shallow landslides involve only unconsolidated slope deposits (i.e., the colluvium), then the rupture surface corresponds to the discontinuity between the bedrock and the overlying loose soil. In this work, based on systematic field observations, we highlight that shallow landslides often involve also portions of the sub-surface bedrock showing different levels of weathering and fracturing. Then, we show that the engineering geological properties of slope deposits, as well as those related to the underlying bedrock, must be considered to obtain more reliable shallow landslides susceptibility assessment. As a first task, a multi-temporal shallow landslide inventory was built by photointerpretation of aerial orthoimages. Then, a new fieldwork-based method is proposed and implemented to acquire, process and spatialize the engineering geological properties of both slope deposits and bedrock. To support the regional scale approach, field observations were collected within, in the neighbour and far from the shallow landslide areas. Finally, both physically-based and data-driven methods were implemented to assess and compare shallow landslide susceptibility at regional scale, as well as to analyse the role of spatial distribution of rock mass quality for shallow slope failure development. The results highlight that, according to geology, structural setting and morphometric conditions, bedrock properties spatially change, defining clusters influencing both the distribution and characters of shallow landslides. As a consequence, the physically-based modelling provides better prediction accuracy when two possible rupture surfaces are analysed, the shallower one located at the slope deposit / bedrock discontinuity, and the deeper one located at the bottom of the fractured and weathered bedrock horizon. Even though the physically-based and data-driven models provide similar results in terms of ROC curves, the resulting susceptibility maps highlight quite substantial differences.

Nowadays it is essential to develop new methodologies to quantify landslide risk, which contribute to the landslide risk management at the municipal level. In this work, a Landslide Risk Index (LRI) is computed for the 278 Portuguese municipalities, which are ranked and characterized according the landslide risk drivers. Landslide risk index was assessed as the product of hazard, exposure and physical vulnerability of buildings scores.The landslide hazard includes the landslide susceptibility evaluated at the national scale using the Information Value method and further validated with prediction-rate curves (Zêzere et al., 2018). Additionally, a weather and climate events index (WCE) was computed using a multicriteria analysis that included the annual frequency of circulation weather types associated to damaging landslides and an extreme precipitation susceptibility index (Santos et al., 2020). Exposure was evaluated for each municipality using the population density (inhabitants/km2) and the road density (km/km2). The physical vulnerability of the buildings was computed using four statistical variables obtained from the official Census: (i) construction technique and construction materials, (ii) reinforced structure, (iii) number of floors and (iv) conservation status. Variable classes were empirically weighted.Exposure is the main driving force of LRI in the metropolitan areas of Lisbon and Porto, whereas the hazard is more relevant in the NW municipalities and the physical vulnerability is the major driving force in the south of the country.For each municipality a landslide risk profile was built, based on the combination of the three driving forces, which can be compared and ranked. Therefore, the landslide risk management strategies at the municipal level must be adjusted to the corresponding dominant drivers in order to reduce landslide impacts.Municipalities with high values of hazard are sensitive to changes on the other risk components, which should draw additional efforts concerning land use management and emergency planning. On the exposure, planning instruments should consider the negative effects on LRI from measures that promote the expansion of people and economic activities towards hazardous zones. On the physical vulnerability, public policies should be aware of the increasing physical vulnerability of buildings in time due to age and lack of maintenance and to public works involving embankments and earthworks.This work contributes to context-oriented strategies of landslide risk management that still lacks in most of the national and regional levels of risk governance processes. Acknowledgements:This work was financed by national funds through FCT—Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide—Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017) and by the Research Unit UIDB/00295/2020. Pedro Pinto Santos is funded by FCT (project reference CEEIND/00268/2017). References:Santos, P.P.; Pereira, S.; Zêzere, J.L.; Tavares, A.O.; Reis, E.; Garcia, R.A.C.; Oliveira, S.C. (2020) A comprehensive approach to understanding flood risk drivers at the municipal level. Journal of Environmental Management (in press).Zêzere, J.L., Oliveira, S.C., Pereira, S., Garcia, R.A.C., Melo, R., Vaz, T., Tavares, A.O., Bateira, C., Santos, P.P., Meneses, B., Quaresma, I. (2018) Construction of a National Landslide Susceptibility Map for Portugal. Geophysical Research Abstracts, Vol. 20, EGU2018-4541.

During the last two centuries, several debris flow events occurred in the upper part of the Zêzere valley, which is located in the Estrela mountain, in Central Portugal. These events were responsible for material damage as well as for the loss of lives. Given the susceptibility of this area to the occurrence of debris flows, a methodology for pedestrian evacuation modelling was implemented, in order to identify buildings at risk and pedestrian travel times to safety areas in a debris flow worst-case scenario. Starting from a dynamic run-out model, developed in previous works, the potential debris flow intensity was estimated (e.g. flow depth, velocity and run-out distance). Sequentially, the buildings potentially affected by the impact of debris flows, as well as the ones where the evacuation would take longer than the debris flows arrival, were identified. In addition, the potentially exposed population was estimated by applying a dasymetric distribution to each residential building. This population distribution took into account the identification of the older residents as the most exposed to debris flows, which is critical to develop reliable pedestrian evacuation travel time scenarios. The pedestrian evacuation modelling was performed using the Pedestrian Evacuation Analyst, a GIS tool developed by the United States Geological Survey. The evacuation modelling was based on an anisotropic approach, which considers the influence of slope direction on travel costs, thus its application is suitable in a mountainous area. The implemented methodology is a critical step towards the implementation of a reliable early warning system to debris flows that can be reproduced elsewhere.Funding information: This work was financed by national funds through FCT—Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide—Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017) and by the Research Unit UIDB/00295/2020. Pedro Pinto Santos is funded by FCT through the project with the reference CEEIND/00268/2017.

This work aims to compute a flood risk index (FRI) for the 278 Portuguese municipalities, designed to rank and characterize the drivers of fluvial flooding-related disasters (Santos et al., 2020). FRI is the product of hazard, exposure and vulnerability scores, where each factor is raised to 1/3, a solution also applied by the INFORM risk index to increase the dispersion of index values.Hazard considers two variables: flood susceptibility (SUSCF), and the weather and climate events index (WCE) translating the frequency of the rainfall events that may generate peak flows. SUSCF is the product of stream flood susceptibility (SFS) (Santos et al., 2019) and the main flood-prone areas (MFPA). SFS considers flow accumulation, slope angle and relative permeability, accounting for the cumulative effect of these factors along the entire basins’ area. MPFA results from overlaying areas with slope angle ≤ 2º and areas with Height Above Nearest Drainage ≤ 2, only when they were topologically connected to streams with SFS > 5.Exposure considers three variables: population density (PD), road density (RD) and the average degree of imperviousness (ADI). PD (inhab./km2) is based on the 2011 Census. RD (km/km2) is calculated from the OpenStreetMap© data. ADI is the municipal average value of the layer “IMD - Imperviousness Degree 2012 – 20 m resolution”, from the Copernicus Land Monitoring Service.Vulnerability (V) is the product of criticality and support capability, where the latter acts by attenuating criticality, according to the methodology presented by Tavares et al. (2018) to assess social vulnerability.The six core variables were scaled to the range [0, 1] following the max-min method. The respective weights were tested and selected according to the scientific literature, correlation and reliability tests.Ward’s clustering classification was used to define seven clusters of municipalities, differing in the scores of hazard, exposure and vulnerability. While it is suggested that municipalities in some clusters would require interventions to reduce hazard, others should invest on medium to long-term measures that address high exposure and vulnerability. The results obtained with this methodological approach contribute to the diversification of flood risk management strategies. Acknowledgements:This work was financed by national funds through FCT—Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide‑Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017) and by the Research Unit UIDB/00295/2020. Pedro Pinto Santos is funded by FCT through the project with the reference CEEIND/00268/2017. References:Santos, P.P., Pereira, S., Zêzere, J.L., Tavares, A.O., Reis, E., Garcia, R.A.C., Oliveira, S.C., 2019. A comprehensive approach to understanding flood risk drivers at the municipal level. J. Environ. Manage. https://doi.org/10.1016/j.jenvman.2020.110127Santos, P.P., Reis, E., Pereira, S., Santos, M., 2019. A flood susceptibility model at the national scale based on multicriteria analysis. Sci. Total Environ. 667, 325–337. https://doi.org/10.1016/j.scitotenv.2019.02.328Tavares, A.O., Barros, J.L., Mendes, J.M., Santos, P.P., Pereira, S., 2018. Decennial comparison of changes in social vulnerability: A municipal analysis in support of risk management. Int. J. Disaster Risk Reduct. 31, 679–690. https://doi.org/10.1016/J.IJDRR.2018.07.009

Landslides, like any natural hazardous process, do not generate risk until some type of interaction with human settlements or activities occur. Consequently, the landslide susceptibility assessment and the quantification of the exposure and potential losses of structures and infrastructures is crucial for informing emergency and spatial planning in areas prone to geomorphological hazards. Thus, the major aim of this work is to identify the current and future buildings’ exposure to landslides, in order to get useful information to support decision planners.The current spatial distribution of buildings and future exposure trends will be assessed based on the Portuguese Census data, which will be used as ancillary information to derive the number and type of buildings at the basic census polygonal unit. The future buildings’ exposure will be determined from a cellular automata simulation model.Four types of buildings are considered: E1 – light structures of wood or metal; E2 – buildings of adobe, rammed earth or loose stone walls; E3 - buildings with brick or stone masonry walls; and E4 - buildings of masonry walls confined with reinforced concrete. The evaluation of buildings’ exposure is made for two landslide event scenarios: one for shallow soil slips (with rupture surfaces depth < 1.5 m) and another one for deep-seated slides (with rupture surfaces depth > 1.5 m). The landslide susceptibility/hazard hotspots for both scenarios are assessed in a study area corresponding to four municipalities located in the northern sector of the Lisbon Metropolitan Area, Portugal. The landslide susceptibility models are based on a bivariate statistical method (Information Value) and on a dataset of eight independent variables assumed as predisposing factors for the occurrence of landslides: lithology, slope, curvature, aspect, slope/contribution area ratio, topographic position index (TPI), soil type and land use. The validation procedures include the computation of ROC curves and the calculation of AUROC. Landslide susceptibility and buildings’ exposure are presented as probabilities at the basic census unit scale. Results combine the probability of occurrence of a landslide with the probability of having a building of a certain type potentially affected by a landslide, for the two landslide event scenarios.Finally, potential losses on buildings are assessed from exposure and damage on buildings caused by landslides in the past.Acknowledgements:This work was financed by national funds through FCT—Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide—Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017) and by the Research Unit UIDB/00295/2020. Pedro Pinto Santos is funded by FCT through the project with the reference CEEIND/00268/2017.

This work proposes a comprehensive methodology to assess rainfall thresholds for landslide initiation, using a centenary landslide database associated with a single centenary daily rainfall dataset. The methodology is applied to the Lisbon region and include the rainfall return period analysis that was used to identify the critical rainfall combination (quantity-duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds is assessed and validated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, such thresholds may be used with acceptable confidence up to 50 km distance from the rain gauge. The obtained rainfall thresholds using linear and potential regression have a good performance in ROC metrics. However, the intermediate thresholds based on the probability of landslide events, established in the zone between the lower limit threshold and the upper limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.

Approaches used to assess shallow slide susceptibility at the basin scale are conceptually different depending on the use of statistical or physically based methods. The former are based on the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the counteracting forces that are resistant to motion. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodologically distinct, the statistical and deterministic methods generate similar shallow slide susceptibility results regarding the model's predictive capacity and spatial agreement; and (ii) the combination of shallow slide susceptibility maps obtained with statistical and physically based methods, for the same study area, generate a more reliable susceptibility model for shallow slide occurrence. These hypotheses were tested at a small test site (13.9 km2) located north of Lisbon (Portugal), using a statistical method (the information value method, IV) and a physically based method (the infinite slope method, IS). The landslide susceptibility maps produced with the statistical and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slide susceptibility map, which combines the independent results obtained with statistical and physically based models. Moreover, the combination of the two models allowed the identification of areas where the results of the information value and the infinite slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.

Approaches used to assess shallow slides susceptibility at the basin scale are conceptually different depending on the use of empirically-based or physically-based methods. The former are sustained by the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the opposing forces that promote resistance to movement. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodological distinct, the statistic and deterministic methods generate similar shallow slides susceptibility results regarding the model’s predictive capacity and spatial agreement; and (ii) the combination of shallow slides susceptibility maps obtained with empirically-based and physically-based methods, for the same study area, generate a more reliable susceptibility model for shallow slides occurrence. These hypotheses were tested in a small test site (13.9 km2) located north of Lisbon (Portugal), using a empirically-based method (the Information Value method) and a physically-based method (the Infinite Slope method). The landslide susceptibility maps produced with the statistic and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross-tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slides susceptibility map, which combines the independent results obtained with empirically-based and physically-based models. Moreover the combination of the two models allowed the identification of areas where the results of the Information Value and the Infinite Slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.