Rainfall Thresholds For Landslides Research Articles

Addressing the Effect of Intra-Seasonal Variations in Developing Rainfall Thresholds for Landslides: An Antecedent Rainfall-Based Approach

We developed a rainfall threshold model with the objective of limiting the effects of uncertainties typically associated with them, such as a lack of robust landslide database, the selection of the contributing rain gauge, seasonal variations in rainfall patterns, and the effect of extreme rainfall conditions. With the aid of gauge-corrected satellite precipitation data and a landslide database compiled from various sources, separate rainfall thresholds were developed for two waves of the monsoon season in the Western Ghats, India. The daily vs. antecedent rainfall distributions for different scenarios of antecedent rainfall were analyzed for landslide occurrence. The different scenarios considered included 1, 2, 3, 5, 10-, 20-, 30- and 40-day antecedent rainfalls along with the monsoon antecedent defined as the cumulative rainfall from the start of the monsoon to the day prior to landslide occurrence, and the event antecedent defined as the cumulative rainfall from the start of a rainfall event to the day prior to landslide occurrence. A statistically defined critical value was used to define the thresholds for extreme rainfall conditions, while ordinary least squares and quantile regression models were compared to identify the best-fit model for the non-extreme rainfall threshold. Receiver Operating Characteristic (ROC) analysis was performed on all these models and the best model was chosen based on the efficiency values. The daily vs. monsoon antecedent threshold was the best model for the first monsoon wave, and the daily vs. event antecedent model was the best model for the second monsoon wave. A separate rainfall threshold was defined for the entire monsoon without subdivision into separate waves, and corresponding ROC statistics were compared with the former approach to analyze the efficacy of intra-seasonal variations in rainfall threshold development. The results suggest that cumulative rainfall makes a significant contribution towards landslide initiation and that intra-seasonal variations should be necessarily considered in rainfall threshold modeling.

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

Integrating field rainfall data with satellite data improves data accuracy and overcomes rainfall data limitations for rain thresholds. Integration can involve field rainfall data, satellite rainfall data, or a different satellite dataset. Merging these rainfall data sources provides more spatial coverage of satellite data. To determine how well rainfall thresholds predict rainfall-triggered landslides, the threshold model must be validated. This study will evaluate satellite rainfall data before and after integration in developing a rainfall threshold model for landslide prediction in Badung Regency. To do so, the study used a cumulative rainfall threshold over 3, 7, 15, and 30 days and two rainfall satellite products (integrated merged multi-satellite retrievals (IMERG) and precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)). Median, first, and third quartiles were used to set thresholds. The area under the curve (AUC) was calculated to validate rainfall threshold outcomes using receiver operating characteristic (ROC) curves. Analysis showed that integrating satellite rainfall data into the rainfall threshold model for landslide prediction yields better results than other methods. An AUC value of 0.903 (90.3%) for the 30-day cumulative rainfall thresholds supports this claim. This model could be a good input for a landslide early warning system in Badung Regency.

Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

Study on Early Identification of Rainfall-Induced Accumulation Landslide Hazards in the Three Gorges Reservoir Area

The early identification of potential hazards is crucial for landslide early warning and prevention and is a key focus and challenging issue in landslide disaster research. The challenges of traditional investigation and identification methods include identifying potential hazards of landslides triggered by heavy rainfall and mapping areas susceptible to landslides based on rainfall conditions. This article focuses on the problem of early identification of rainfall-induced accumulation landslide hazards and an early identification method is proposed, which is “first identifying the accumulation that is prone to landslides and then determining the associated rainfall conditions”. This method is based on identifying the distribution and thickness of accumulation, analyzing the rainfall conditions that trigger landslides with varying characteristics, and establishing rainfall thresholds for landslides with different accumulation characteristics, ultimately aiming to achieve early identification of accumulation landslide hazards. In this study, we focus on the Zigui section of the Three Gorges Reservoir as study the area, and eight main factors that influence the distribution and thickness of accumulation are extracted from multi-source data, then the relative thickness information extraction model of accumulation is established by using the BP neural network method. The accumulation distribution and relative thickness map of the study area are generated, and the study area is divided into rocky area (less than 1 m), thin (1 to 5 m), medium (5 to 10 m), and thick area (thicker than 10 m) according to accumulation thickness. Rainfall is a significant trigger for landslide hazards. It increases the weight of the sliding mass and decreases the shear strength of soil and rock layers, thus contributing to landslide events. Data on 101 rainfall-induced accumulation landslides in the Three Gorges Reservoir area and rainfall data for the 10 days prior to each landslide event were collected. The critical rainfall thresholds corresponding to a 90% probability of landslide occurrence with different characteristics were determined using the I-D threshold curve method. Prediction maps of accumulation landslide hazards under various rainfall conditions were generated by analyzing the rainfall threshold for landslides in the Three Gorges Reservoir area, serving as a basis for early identification of rainfall-induced accumulation landslides in the region. The research provides a method for the early identification of landslides caused by heavy rainfall, delineating landslide hazards under different rainfall conditions, and providing a basis for scientific responses, work arrangements, and disaster prevention and mitigation of landslides caused by heavy rainfall.

Quantitative analysis of rainfall events for potential rapid landslides and flashfloods in brazilian municipalities

This study examines rainfall events in Brazil, with emphasis on their potential to induce geo-hydrological hazards, like landslides and flash floods. Utilizing the CHIRPS dataset and rainfall thresholds for landslides, we quantify and analyze climatic hazards at a national level, providing perspective into the relationship between precipitation patterns and geo-hydrological risks. Our findings aim to contribute to a holistic understanding of climatic risk, guiding strategic decisions for disaster risk reduction and climate change adaptation.

Physically-based rainfall-induced landslide thresholds for the Tianshui area of Loess Plateau, China by TRIGRS model

Physically-based models are widely recognized as valuable tools for analyzing slope stability and determining rainfall thresholds for landslides. In this study, a MATLAB program that utilizes the physically-based TRIGRS model to define rainfall intensity and duration thresholds is presented in the loess watershed basin of the Tianshui area, Gansu Province, China. We calculated the critical rainfall intensity (Ic) and duration conditions (D) that predict failure in each grid cell of 21 different rainfall intensity scenarios ranging from 1 mm/h to 100 mm/h and then fitted equation parameters (scale parameter α and shape parameter β) of the threshold curves by power-law equations. By considering the equation parameters within different hillslope gradient ranges, the rainfall threshold curves accounting for geological and geomorphological factors are ultimately established for the loess area. Our findings reveal a negative correlation between the scale parameter α and the shape parameter β. As α increases, β decreases until a specific threshold value of α is reached, at which β remains constant. A significant impact of hillslope gradient conditions on the shape of the threshold curves is observed. As the hillslope gradient increases, the equation parameters decrease. Furthermore, the position of the rainfall threshold exhibits considerable variation with respect to different lithological types. This analysis may help more deeply understand the different factors affecting the physically-based threshold variability, and the threshold curves can be a good alternative for performing the early warning of rainfall-induced landslides in loess areas.

Definition of Rainfall Thresholds for Landslides Using Unbalanced Datasets: Two Case Studies in Shaanxi Province, China

The Lueyang and Xunyang counties in the Shaanxi province (China) are highly susceptible to rainfall-induced landslides. Rainfall thresholds are the most used tool to predict the occurrence of rainfall-induced landslides over large areas. However, the definition of robust thresholds may be difficult for unbalanced datasets, for which the number of non-landslide observations is much higher than the number of landslide observations. This study aims at defining adequate rainfall thresholds for the two study areas using landslide datasets that are strongly unbalanced in terms of occurrences vs. non-occurrences. Two types of rainfall thresholds are determined using a frequentist method at several non-exceedance and exceedance probabilities, separately considering rainfall events responsible for landslides (positive thresholds) and rainfall events not responsible for landslides (negative thresholds). The comparison between the two sets of thresholds shows that the method based on non-triggering events allows defining rainfall thresholds characterized by lower uncertainties and a better performance than the ones defined considering the triggering events, in both the study areas. In particular, the best-performing thresholds are the negative threshold defined at 15% exceedance probability for the Lueyang county and the negative threshold defined at 20% exceedance probability for the Xunyang county.

Using Deep Learning to Formulate the Landslide Rainfall Threshold of the Potential Large-Scale Landslide

The complex and extensive mechanism of landslides and their direct connection to climate change have turned these hazards into critical events on a global scale, which can have significant negative influences on the long-term sustainable development of nations. Taiwan experiences numerous landslides on different scales almost every year. However, Typhoon Morakot (2009), with large-scale landslides that trapped people, demonstrated the importance of an early warning system. The absence of an effective warning system for landslides along with the impossibility of its accurate monitoring highlighted the necessity of landslide rainfall threshold prediction. Accordingly, the prediction of the landslide rainfall threshold as an early warning system could be an effective tool with which to develop an emergency evacuation protocol. The purpose of this study is to present the capability of the deep learning algorithm to determine the distribution of landslide rainfall thresholds in a potential large-scale landslide area and to assess the distribution of recurrence intervals using probability density functions, as well as to assist decision makers in early responses to landslides and reduce the risk of large-scale landslides. Therefore, the algorithm was developed for one of the potential large-scale landslide areas (the Alishan D098 sub-basin), Taiwan, which is classified as a Type II Landslide Priority Area. The historical landslide data, maximum daily rainfall, 11 topographic factors from 2004 to 2017, and the Keras application programming interface (API) python library were used to develop two deep learning models for landslide susceptibility classification and landslide rainfall threshold regression. The predicted result shows the lowest landslide rainfall threshold is located primarily in the northeastern downstream of the Alishan catchment, which poses an extreme risk to the residential area located upstream of the landslide area, particularly if large-scale landslides were to be triggered upstream of Alishan. The landslide rainfall threshold under controlled conditions was estimated at 780 mm/day (20-year recurrence interval), or 820 mm/day (25-year recurrence interval). Since the frequency of extreme rainfall events caused by climate change is expected to rise in the future, the overall landslide rainfall threshold was considered 980 mm/day for the entire area.

Hydrological regimes in different slope environments and implications on rainfall thresholds triggering shallow landslides

Assessing hazard of rainfall-induced shallow landslides represents a challenge for the risk management of urbanized areas for which the setting up of early warning systems, based on the reconstruction of reliable rainfall thresholds and rainfall monitoring, is a solution more practicable than the delocalization of settlements and infrastructures. Consequently, the reduction in uncertainties affecting the estimation of rainfall thresholds conditions, leading to the triggering of slope instabilities, is a fundament task to be tackled. In such a view, coupled soil hydrological monitoring and physics-based modeling approaches are presented for estimating rainfall thresholds in two different geomorphological environments prone to shallow landsliding. Based on the comparison of results achieved for silty–clayey soils characterizing Oltrepò Pavese area (northern Italy) and ash-fall pyroclastic soils mantling slopes of Sarno Mountains ridge (southern Italy), this research advances the understanding of the slope hydrological response in triggering shallow landslides. Among the principal results is the comprehension that, mainly depending on geological and geomorphological settings, geotechnical and hydrological properties of soil coverings have a fundamental control on the timing and intensity of hydrological processes leading to landslide initiation. Moreover, results obtained show how the characteristics of the soil coverings control the slope hydrological response at different time scales, making the antecedent soil hydrological conditions a not negligible factor for estimating landslide rainfall thresholds. The approaches proposed can be conceived as an adaptable tool to assess hazard to initiation of shallow rainfall-induced landslides and to implement early-warning systems from site-specific to distributed (catchment or larger) scales.

Probabilistic rainfall threshold of landslides in Data-Scarce mountainous Areas: A case study of the Bailong River Basin, China

The damage caused by rainfall-induced landslides has increased globally. The development of urbanisation has led to the expansion of residential areas in mountainous areas, and human activities have accelerated slope instability. However, the limited data records in mountainous zones have resulted in low-precision landslide rainfall thresholds. Establishing a landslide early warning model in areas with scarce data is an unresolved problem. This study uses the Bailong River Basin in western China as an example. By downscaling Tropical Rainfall Measuring Mission (TRMM) data to a daily resolution of 1 km and conditionally merging it with ground station rainfall, we obtain high-precision rainfall data to establish rainfall threshold curves based on Bayes' theorem and the frequency method. Subsequently, a landslide susceptibility map based on deep learning was integrated to construct a regional landslide early warning model. The results are as follows: First, compared with downscaling rainfall and rain gauge interpolation data, the daily rainfall data with a resolution of 1 km that combines the two has better overall performance and higher accuracy. Second, we generated the event-rainfall-and-duration threshold curves of 5%, 20%, and 50% probability by combining the frequency method and Bayesian theorem, and show that with a probability of landslide occurrence of 50%, while the intercept of Bayes' theorem is greater than that of the frequency method, the overall trend is consistent. Finally, by using a typical mass rainfall event to test the performance, the results show that the early warning capability of the model integrated with the rainfall threshold and landslide susceptibility map is more accurate than that of using a separate rainfall threshold. The outcomes of this research are expected to provide efficient support for early warning and risk management of geological disasters in mountainous areas with scarce data.

Physically based and distributed rainfall intensity and duration thresholds for shallow landslides

For landslide assessment in a specific small area of interest, regional thresholds may not be applicable and the application of empirical-statistical methodologies could be limited due to a lack of required databases (landslide inventories and triggering rainfall events). Rainfall thresholds for landslides have been recently derived from the implementation of physically based methodologies that usually provide a power-law equation for the study area (e.g. region or a specific basin). In this work, a new methodology for defining rainfall intensity and duration thresholds using a physically based model (TRIGRS) is presented and implemented in a tropical mountain basin of the Colombian Andes. Multiple calculations of the factor of safety for different (synthetic) rainfall events permitted the fitting of power-law equations to the sets of critical intensity and duration conditions, causing failure in each grid cell. Maps of the equation parameters (scale and shape) and the range of applicability (initial and final duration) of the grid cell thresholds were analysed. The results show that the hydraulic and mechanical properties of the soil affected the threshold curve characteristics, comparing sandy soil with silty loam; the higher strength characteristics of sandy soil meant that most extreme rainfall conditions are required to reach their thresholds. This methodology could improve landslide early warning systems in terms of analysing the specific (local) areas that are highly susceptible to landslide occurrence.

Improved rainfall threshold for landslides in data sparse and diverse geomorphic milieu: a cluster analysis based approach

Rainfall-triggered landslides are the most common type of mass movement seen along the tropical belt due to the prevalence of monsoons. These landslides can be forecasted by understanding the spatial and temporal rainfall distribution patterns, and subsequent generation of rainfall threshold (RT). However, deriving a regional RT in a geologically, geographically and physiographically diverse milieu is a formidable task. The data on spatial and intra-seasonal variability of monsoons can be widely dispersed in such diversified terrains. Clustering analysis provides a promising approach to handle such widely dispersed data. This study intends to develop a methodology using 2-stage clustering process to create RT in such terrains by using daily rainfall versus antecedent rainfall and rainfall versus antecedent rainfall versus soil depth. Sixteen rainfall-induced landslides, located in different terrains in the Western Ghats of India, were subjected to this analysis. Majority of the landslides were modeled, and different RTs were derived for different conditions. The landslides belong to four different classes, viz., landslides occurring at steep slopes; those occurring at knickpoints of highland and midland; in the plateau region and others characterized by a thin veneer of soil. Out of 16 landslides subjected to RT, this method was able to model 13 landslides with a success rate of 81.25%, which is a fair figure.

Rainfall Event–Duration Thresholds for Landslide Occurrences in China

A rainfall threshold for landslide occurrence at a national scale in China has rarely been developed in the early warning system for landslides. Based on 771 landslide events that occurred in China during 1998–2017, four groups of rainfall thresholds at different quantile levels of the quantile regression for landslide occurrences in China are defined, which include the original rainfall event–duration (E–D) thresholds and normalized (the accumulated rainfall is normalized by mean annual precipitation) (EMAP–D) rainfall thresholds based on the merged rainfall and the Climate Prediction Center Morphing technique (CMORPH) rainfall products, respectively. Each group consists of four sub-thresholds in rainy season and non-rainy season, and both are divided into short duration (<48 h) and long duration (≥48 h). The results show that the slope of the regression line for the thresholds in the events with long durations is larger than that with short durations. In addition, the rainfall thresholds in the non-rainy season are generally lower than those in the rainy season. The E–D thresholds defined in this paper are generally lower than other thresholds in previous studies on a global scale, and a regional or national scale in China. This might be due to there being more landslide events used in this paper, as well as the combined effects of special geological environment, climate condition and human activities in China. Compared with the previous landslide model, the positive rates of the rainfall thresholds for landslides have increased by 16%–20%, 10%–17% and 20%–38% in the whole year, rainy season and non-rainy season, respectively.

Influence of hydraulic properties on physically modelling slope stability and the definition of rainfall thresholds for shallow landslides

Physically-based models are recognized as a good tool for slope stability analysis and have been implemented for the definition of rainfall thresholds for landslides. In this study, a methodology for estimating rainfall intensity and duration thresholds for shallow sliding failure, using the physical model TRIGRS, was implemented in a tropical mountainous basin of the Valle de Aburrá (Colombia). The variation of pressure head and factor of safety during different rainfall events and hydraulic conditions is studied for specific grid cells. Steady state and a maximum pressure head were reached for each grid cell regardless of the different rainfall intensities and hydraulic conductivities simulated, but the time in which this condition is reached depends on these variables and occurs faster for steeper slopes. The influence of hydraulic properties on the stability results and the position of the rainfall intensity and duration thresholds were studied with parametric analysis. Very low values of saturated hydraulic conductivity do not allow rainfall intensity and duration thresholds to be developed. The rainfall threshold position also has a greater variation with respect to the parameters of the soil water retention curve and the initial infiltration rate (or long-term background rainfall rate) for low values of saturated hydraulic conductivity.

Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding

Determining rainfall thresholds for landsliding is crucial in landslide hazard evaluation and early warning system development, yet challenging in data-scarce regions. Using freely available satellite rainfall data in a reproducible automated procedure, the bootstrap-based frequentist threshold approach, coupling antecedent rainfall (AR) and landslide susceptibility data as proposed by Monsieurs et al., has proved to provide a physically meaningful regional AR threshold equation in the western branch of the East African Rift. However, previous studies could only rely on global- and continental-scale rainfall and susceptibility data. Here, we use newly available regional-scale susceptibility data to test the robustness of the method to different data configurations. This leads us to improve the threshold method through using stratified data selection to better exploit the data distribution over the whole range of susceptibility. In addition, we discuss the effect of outliers in small data sets on the estimation of parameter uncertainties and the interest of not using the bootstrap technique in such cases. Thus improved, the method effectiveness shows strongly reduced sensitivity to the used susceptibility data and is satisfyingly validated by new landslide occurrences in the East African Rift, therefore successfully passing first transferability tests.

Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India

Sikkim, one of the Northeastern states of India, is a famous tourism spot in the Himalayas with dynamic population density. This mountainous area receives heavy rainfall and is well known for frequent shallow landslides, especially, Chandmari, which is a village, situated in Gangtok area in East Sikkim. Even though it is well known that rainfall and landslides are correlated, Sikkim lacks a well-established landslide early warning system. Such a system is important in this region because it is one of the highest landslide-prone areas in India. The current research attempts to establish rainfall thresholds as part of developing an efficient landslide early warning system for this region. The rainfall thresholds for landslides are derived based on the daily rainfall data available from India Meteorological Department (IMD) for six stations in Sikkim. Analysis of daily rainfall data and landslide events in this area between the year 1990 and 2017 is performed. An intensity–duration (I–D)-based regional rainfall threshold is derived as I = 43.26 D−0.78 (I = rainfall intensity in mm/day and D = duration in days) for the rainfall-triggered landslides in Sikkim region and a local threshold of I = 100 D−.92 was developed for the Gangtok area. Furthermore, the influence of antecedent rainfall in landslide initiation is explored by considering the daily, 3-day, 5-day, 7-day, and 20-day cumulative rainfall values associated with landslides. The proposed threshold equations and study of the effect of antecedent rainfall on landslides are intended to aid in enhancing the real-time landslide early warning system (R-LEWS) being developed for Sikkim.

Assessment of Rainfall Thresholds for Rain-Induced Landslide Activity in North Sikkim Road Corridor in Sikkim Himalaya, India

Land sliding is a perennial problem in the Eastern Himalayas. Out of 0.42 million km2 of Indian landmass prone to landslide, 42% fall in the North East Himalaya, specially Darjeeling and Sikkim Himalaya. Most of these landslides are triggered by excessive monsoon rainfall between June and October in almost every year. Various attempts in the global scenario have been made to establish rainfall thresholds in terms of intensity – duration of antecedent rainfall models on global, regional and local scale for triggering of the landslide. This paper describes local aspect of rainfall threshold for landslides based on daily rainfall data in and around north Sikkim road corridor region. Among 210 Landslides occurring from 2010 to 2016 were studied to analyze rainfall thresholds. Out of the 210 landslides, however, only 155 Landslides associated with rainfall data which were analyzed to yield a threshold relationship between rainfall intensity-duration and landslide initiation. The threshold relationship determined fits to lower boundary of the Landslide triggering rainfall events is I = 4.045 D - 0.25 (I=rainfall intensity (mm/h) and D=duration in (h)), revealed that for rainfall event of short time (24 h) duration with a rainfall intensity of 1.82 mm/h, the risk of landslides on this road corridor of the terrain is expected to be high. It is also observed that an intensity of 58 mm and 139 mm for 10-day and 20-day antecedent rainfall are required for the initiation of landslides in the study area. This threshold would help in improvement on traffic guidance and provide safety to the travelling tourists in this road corridor during the monsoon.

Extracting region-specific runout behavior and rainfall thresholds for massive landslides using seismic records: a case study in southern Taiwan

Typhoon Morakot struck Taiwan on 7 August 2009, bringing severe rainfall and landslides. Of the many landslides caused by the storm, 12 large-scale landslides were discovered based on seismic signals recorded from broadband seismic stations. Landslides inventoried from both seismic records and satellite imagery are used as baseline data to evaluate region-specific forensic analyses of landslide features, e.g. disturbed area, width and runout distance (Drunout), associated with landslide-induced seismic signals and signal durations (Δt). The results of these analyses show that (i) the landslide-disturbed area correlated well with signal duration when Δt was over 60 s; (ii) the landslide-disturbed area correlated well with the ratio of the landslide width to runout distance when Δt was under 40 s; (iii) the runout distance of sliding mass exhibited a positive relation with Δt2, satisfying the energy conserved during mass movement. This case study presents a region-specific examination of the Δt-Drunout relationship based on 12 large-scale landslides that occurred during Typhoon Morakot, providing insight into the sliding processes. In addition, the time of landslide initiation was extracted from seismic signals in order to better understand region-specific, large-scale, landslide rainfall thresholds. These relationships indicate that (a) long-duration rainstorms with high cumulative rainfall tend to dictate the occurrence of large-scale landslides; (b) the rainfall threshold for large-scale landslides is found to be higher than in previous studies that also include smaller landslides, defined in this region as I = 60D−0.31, where I is the rainfall intensity (mm/h) and D is the duration (h) of the rainstorm.

Rainfall thresholds as a landslide indicator for engineered slopes on the Irish Rail network

Rainfall thresholds express the minimum levels of rainfall that need to be reached or exceeded in order for landslides to occur in a particular area. They are a common tool in expressing the temporal portion of landslide hazard analysis. Numerous rainfall thresholds have been developed for different areas worldwide, however none of these are focused on landslides occurring on the engineered slopes on transport infrastructure networks. This paper uses empirical method to develop the rainfall thresholds for landslides on the Irish Rail network earthworks. For comparison, rainfall thresholds are also developed for natural terrain in Ireland. The results show that particular thresholds involving relatively low rainfall intensities are applicable for Ireland, owing to the specific climate. Furthermore, the comparison shows that rainfall thresholds for engineered slopes are lower than those for landslides occurring on the natural terrain. This has severe implications as it indicates that there is a significant risk involved when using generic weather alerts (developed largely for natural terrain) for infrastructure management, and showcases the need for developing railway and road specific rainfall thresholds for landslides.

Comparison of statistical methods and multi-time validation for the determination of the shallow landslide rainfall thresholds

Shallow landslides are unforeseeable phenomena often resulting in critical conditions in terms of people’s safety and damage. The main purpose of this paper is the comparison of different statistical methods used to determine the rainfall thresholds for the shallow landslide occurrence. Rainfall data over a 46-year period were collected for one rain gauge located in a test area of northwest Italy (Riviera Spezzina; RS). In the RS, intense rainfalls often induce shallow landslides causing damage and sometimes casualties. The rainfall events occurred in the 1967–2006 period were classified as events inducing shallow landslides (SLEs1967–2006) and events that did not trigger shallow landslides (NSLEs1967–2006). Thresholds for various percentiles of SLEs1967–2006 were computed by identifying the lower limit above which shallow landslides occurred. Another set of thresholds, corresponding to different probabilities of occurrence, was determined using SLEs1967–2006 and NSLEs1967–2006. The least-squares linear fit (LSF) and the quantile regression (QR) techniques were employed in the former approach, while the logistic regression (LR) was applied in the latter. The thresholds were validated with the same data used for their definition and with the data recorded in the 2008–2014 period. Contingency tables were created and contingencies and skill scores were computed. The 10% probability threshold obtained using the LR method is characterized by the best values of at least two skill scores for both periods considered; therefore, it may be considered the “best” threshold for the RS. The results of this work can help the choice of the best statistical method to determine the shallow landslide rainfall thresholds.