Empirical Rainfall Thresholds Research Articles

A semi-automatic interpretation method for utilizing InSAR results to recognize active landslides considering causative factors

Synthetic Aperture Radar Interferometry (InSAR), which can map subtle ground displacement over large areas, has been widely utilized to recognize active landslides. Nevertheless, due to various origins of subtle ground displacement, their presence on slopes may not always reflect the occurrence of active landslides. Therefore, interpretation of exact landslide-correlated deformation from InSAR results can be very challenging, especially in mountainous areas, where natural phenomenon like soil creep, anthropogenic activities and erroneous deformational signals accumulated during InSAR processing can easily lead to misinterpretation. In this paper, a two-phase interpretation method applicable to regional-scale active landslide recognition utilizing InSAR results is presented. The first phase utilizes statistical threshold and clustering analysis to detect unstable regions mapped by InSAR. The second phase introduces landslide susceptibility combined with empirical rainfall threshold, which are considered as causative factors for active landslides triggered by rainfall, to screen unstable regions indicative of active landslides. A case study validated by field survey indicates that the proposed interpretation method, when compared to a baseline model reported in the literature, can achieve better interpretation accuracy and miss rate.

Landslides Rainfall One of the Main Triggering Factors in the Mountainous Regions of Puebla, Mexico

The rains are one of the main triggers of landslide phenomena in the mountainous region of Puebla, which values reach up 4500 mm/year in some places in this area. Rainfalls combined with other factors such as morphology (mountainous topographic relief with strong inclinations), strongly folded sedimentary rocks (conglomerates, sandstones, siltstones, and limestones), rock discontinuities (fissures, cracks, joints, stratigraphic planes, etc.) and human activity (cuts, excavations, overloads, mining, water discharge, deforestation, land use change, etc.), offer a scenario prone to landslide phenomena that cause human casualties, economic and environmental losses. The landslides disasters have affected many social sectors from these communities (housing, education, health, hydraulic infrastructure, electricity, communication, transportation, public services, agriculture, forestry, etc.), observing that in these sites there are communities of ethnic groups Nahuatls, Totonacs, Otomies and Tepehuas who build their houses out of sheet metal, cardboard, plastic and wood where any natural phenomenon represents a true disaster. This article presents the relationship between hurricanes and torrential rains with the disasters caused in communities in the mountainous region of Puebla. From the analysis of the data obtained from the different meteorological stations installed in the region of interest during a period of 20 years (1999 – 2019), an empirical rainfall threshold of 100 mm/day was determined as a trigger for landslides in the region of study. The aforementioned value is very important data for Civil Protection Government Authorities in order to establish actions and strategies for mitigation and risk management due to landslides. From this rain threshold, evacuation actions are put into operation to reduce the vulnerability of exposed and fragile communities which are immediately transferred to protection shelters

An empirical rainfall threshold approach for the civil protection flood warning system on the Milan urban area

Non-structural risk mitigation tools such as civil protection alerts for citizens proves highly beneficial in minimizing the impacts linked to floods. Flood forecasting represents a challenge due to complex and non-linear hydrological processes involved, especially in highly urbanized areas. In this study, a Flood Warning System (FWS) based on the development of catchment-specific empirical Rainfall Thresholds (RTs) is proposed.Seven river catchments in the “Hydraulic node of Milan,” northern Italy, were analyzed using a dataset of 25 years (1998–2022) of hourly rainfall and discharge data.An empirical methodology, based only on historical rainfall-runoff data and applicable to any river catchment, is proposed with the aim to validate and improve the existing Rainfall Threshold (RT) defined on the same area by the Lombardy Region civil protection.The RTs obtained using the proposed method showed improvements with respect to the existing civil protection RTs, because it allows to derive time-continuous and catchment-specific RTs. Additionally, accounting for the Antecedent Moisture Conditions (AMC) with the proposed “equivalent rainfall” approach results in more accurate RTs, suggesting its consideration for issuing civil protection alerts. The accuracy and uncertainty of the RTs were analyzed by means of binary classification measures coupled with bootstrap resampling.The proposed procedure for constructing RTs, which is applicable to any river catchment having sufficiently long time series of rainfall and runoff data, and not necessarily on urban areas only, indicates potential for being an additional and simple FWS to mitigate flood risks for civil protection purposes.

Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

This study aims to derive and evaluate new empirical rainfall thresholds as the basis for landslide early warning in Progo Catchment, Indonesia, using high-resolution rainfall datasets. Although attempts have been made to determine such thresholds for regions in Indonesia, they used coarse-resolution data and fixed rainfall duration that might not reflect the characteristics of rainfall events that induced the landslides. Therefore, we evaluated gauge-adjusted global satellite mapping of precipitation (GSMaP-GNRT) and bias-corrected climate prediction center morphing method (CMORPH-CRT) hourly rainfall estimates against measurements at rainfall stations. Based on this evaluation, a minimum rainfall of 0.2 mm/h was used to identify rain events, in addition to a minimum of 24 h of consecutive no-rain to separate two rainfall events. Rainfall thresholds were determined at various levels of non-exceedance probability, using accumulated and duration of rainfall events corresponding to 213 landslide occurrences from 2012 to 2021 compiled in this study. Receiver operating characteristics (ROC) analysis showed that thresholds based on rainfall station data, GSMaP-GNRT, and CMORPH-CRT resulted in area under ROC curve values of 0.72, 0.73, and 0.64, respectively. This result indicates that the performance of high-resolution satellite-derived data is comparable to that of ground observations in the Progo Catchment. However, GSMaP-GNRT outperformed CMORPH-CRT in discriminating the occurrence/non-occurrence of landslide-triggering rainfall events. For early warning purposes, the rainfall threshold is selected based on the probability exlevel at which the threshold maximizes the true skill score, i.e., at 10% if based on station data, or at 20% if based on GSMaP-GNRT.

Objective definition of discharge thresholds for post-fire debris flows

Runoff-generated debris flows are a common post-fire hazard in the western United States and a growing number of regions around the world. As wildfire continues to emerge across a broader range of geographic regions and plant communities, there is an increasing need for generalizable methods to predict post-fire debris-flow initiation. The prediction of post-fire debris flow during intense rainstorms has traditionally relied upon empirical rainfall thresholds. Rainfall intensity-duration thresholds are often developed based on rainfall data and the hydrologic response to those rainstorms. They are most applicable to the specific regions where data are collected. Here, we present a new predictive approach that utilises processes-based models with fundamental physics and machine learning methods to estimate discharge thresholds for runoff-generated debris-flow initiation in four recently burned areas in the western United States. We assess the performance of the objectively defined discharge threshold-based predictions for post-fire debris-flow initiation from our hybrid framework, which utilises debris-flow timing within rainstorms, physically based numerical simulations of runoff, and the support vector machines method. The proposed thresholds have a good balance between true and false predictions for debris flow and floods. Importantly, our method permits the direct estimation of rainfall intensity-duration thresholds for areas where post-fire debris flow observations are limited.

Towards Establishing Empirical Rainfall Thresholds for Shallow Landslides in Guangzhou, Guangdong Province, China

Empirical rainfall thresholds for predicting rainfall-triggered shallow landslides are proposed for Guangzhou city, which is prone to widespread geological hazards during the annual flood season due to the subtropical monsoon climate and frequent tropical storms and typhoons. In this study, the cumulated event rainfall (E, in mm), the duration of rainfall event (D, in day) (E–D) thresholds, normalized cumulated event rainfall, and the duration of rainfall event (EMAP–D) thresholds were defined. Thresholds based on five lithological units were obtained at 5%, 20%, and 50% probability levels using quantile regression methods. More than two-thirds of the landslides occurred within units of intrusive rock. The 20-day cumulative rainfall of 97 mm integrating cumulative event rainfall and the duration of rainfall events (CED) is introduced into the three-dimensional spatial threshold. The areas under the receiver operating characteristic curves for the CED threshold and E–D threshold were 0.944 and 0.914, respectively, and the true-positive rate of the CED threshold with the same probability level was slightly lower than that of the E–D threshold, but the CED threshold false-positive rate was much better than the E–D threshold, which can significantly reduce false alarm rate since many non-triggering rainfalls were filtered out.

Evaluation of potential changes in landslide susceptibility and landslide occurrence frequency in China under climate change

Climate change can alter the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In recent decades, China suffered great human and economic losses due to rainfall-induced landslides. However, how the landslide hazard situation will evolve in the future is still unclear, also because of sparse comprehensive evaluations of potential changes in landslide susceptibility and landslide occurrence frequency under climate change. This study builds upon observed and modelled rainfall data from 24 bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs), a statistical landslide susceptibility model, and empirical rainfall thresholds for landslide initiation, to evaluate changes in landslide susceptibility and landslide occurrence frequency at national-scale. Based on four Shared Socioeconomic Pathways (SSP) scenarios, changes in the rainfall regime are projected and used to evaluate subsequent alterations in landslide susceptibility and in the frequency of rainfall events exceeding empirical rainfall thresholds. In general, the results indicate that the extend of landslide susceptible terrain and the frequency of landslide-triggering rainfall will increase under climate change. Nevertheless, a closer inspection provides a spatially heterogeneous picture on how these landslide occurrence indicators may evolve across China. Until the late 21st century (2080–2099) and depending on the SSP scenarios, the mean annual precipitation is projected to increase by 13.4 % to 28.6 %, inducing an 1.3 % to 2.7 % increase in the modelled areal extent of moderately to very highly susceptible terrain. Different SSP scenarios were associated with an increase in the frequency of landslide-triggering rainfall events by 10.3 % to 19.8 % with respect to historical baseline. Spatially, the southeastern Tibetan Plateau and the Tianshan Mountains in Northwestern Basins are projected to experience the largest increase in landslide susceptibility and frequency of landslide-triggering rainfall, especially under the high emission scenarios. Adaptation and mitigation methods should be prioritized for these future landslide hotspots. This work provides a better understanding of potential impacts of climate change on landslide hazard across China and represents a first step towards national-scale quantitative landslide exposure and risk assessment under climate change.

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

Shallow landslides due to the soil saturation induced by intense rainfall events are very common in northern Italy, particularly in the Alps and Prealps. They are usually triggered during heavy rainstorms, causing severe damage to property, and sometimes causing casualties. A historical study and analysis of shallow landslides and mud-debris flows triggered by rainfall events in Lombardy was carried out for the period of 1911–2010, over an area of 14,019 km2. In this study, intensity–duration rainfall thresholds have been defined using the frequentist approach, considering some pedological characteristics available in regional soil-related databases, such as the soil region, the textural class, and the dominant soil typological units (STU). The soil-based empirical rainfall thresholds obtained considering the soil regions of the study area were significantly different, with a lower threshold for landslide occurrence in the soil region M1 (Alps), where soils developed over siliceous parent material, with respect to the whole study area and the soil region M2 (Prealps), where soils developed over calcareous bedrocks. Furthermore, by considering textural classes, the curves were differentiated, with coarse-textured soils found more likely to triggerlandslides than fine soils. Finally, considering both texture and main soil groups, given the same rainfall duration, the rainfall amount and intensity needed to initiate a landslide increased in the following order: “coarse-skeletal” Cambisols < Umbrisols < Podzols < “fine” Cambisols. The results of this study highlighted the relevant role of pedological conditioning factors in differentiating the activation of rainfall-induced shallow landslides in a definite region. The information on soils can be used to define more precise rainfall–pedological thresholds than empirical thresholds based solely on meteorological conditions, even when they are locally defined. This knowledge is crucial for forecasting and preventing geo-hydrological processes and in developing better warning strategies to mitigate risks and to reduce socio-economic damage.

Investigation on the Failure Mechanism of Rainfall-Induced Long-Runout Landslide at Upputhode, Kerala State of India

The main objective of the study is to estimate the shear resistance mobilized on the slope surface under large deformation and to identify the failure mechanism of the landslide through the simulation model. The field investigations were carried out using Geophysical tests, and the laboratory tests were conducted to identify the engineering properties of the soil with weathering characteristics of the parent rock. The residual shear strength parameters from Torsional ring shear tests were used in LS-RAPID numerical simulation software to study the mechanism of the landslide. The critical pore water pressure ratio (ru = 0.32) required for the initiation of a landslide was obtained. The increase in pore water pressure reduces the soil matric suction and thereby results in the reduction of the shear strength of the soil. The progressive failure mechanism and the three landslide processes (initiation, run out and deposition) are investigated. The velocity of the moving landslide mass in the role of demolishing the building is studied and helps in finding suitable remedial measures for the nearby building. The empirical rainfall threshold based on the antecedent rainfall was developed and revealed that either a high daily rainfall intensity of 142 mm without any antecedent rainfall, or an antecedent rainfall of 151 mm for a cumulative period of 5 days with even continuous normal rainfall can initiate landslide.

Empirically Based Rainfall Threshold for Landslides Occurrence in Peninsular Malaysia

Empirical rainfall thresholds for the purpose of shallow landslide forecasting are proposed for Peninsular Malaysia where numerous slope failures are reported due to the intense rainfall in conjunction with the humid tropical climate. Thirty-seven cases of landslide-triggering-rainfall were selected from 1993 to 2018 to identify the correlation between rainfall and shallow landslide through the analysis of specific rainfall events. The derived rainfall parameters were applied to establish two rainfall thresholds of (Imean-D) and (Imax-D) via practical methods. For the identical range of event duration 1 < D < 263 h, the (Imean-D) threshold formula was expressed as I = 17.5 D−0.722, while the (Imax-D) threshold was defined as I = 37.8 D−0.114. Both thresholds performed different functionalities with a primary goal of predicting shallow landslides. When both (Imean-D) and (Imax-D) thresholds were compared with the thresholds proposed by various studies worldwide, both dominated the upper positions. More rainfall is required for land sliding due to the high thickness of the Malaysian soil that is associated with the abundant tropical downpour. From the perspective of the antecedent, the period of prolonged precipitation or short heavy rainfall from 1 to 10 days can result in shallow landslides for Peninsular Malaysia. In the context of geology, the igneous rock type of granite has the highest susceptibility to the shallow landslide at 65%, despite other rock types of sedimentary and metamorphic. The threshold validation depicted all True Positive events for the (Imax-D) threshold, and one Negative False event for the (Imean-D) threshold. The (Imean-D) threshold was revised to acquire the new value, but it needed to deal with the possibility of False Alarm and the (Imax-D) threshold seemed to be more credible to represent the rainfall-induced shallow landslide threshold for Peninsular Malaysia.

Reanalysis of Soil Moisture Used for Rainfall Thresholds for Rainfall-Induced Landslides: The Italian Case Study

Landslides are one of the most frequent natural disasters that can endanger human lives and property. Therefore, prediction of landslides is essential to reduce economic damage and save human lives. Numerous methods have been developed for the prediction of landslides triggering, ranging from simple methods that include empirical rainfall thresholds, to more complex ones that use sophisticated physically- or conceptually-based models. Reanalysis of soil moisture data could be one option to improve landslide forecasting accuracy. This study used the publicly available FraneItalia database hat contains almost 9000 landslide events that occurred in the 2010–2017 period in Italy. The Copernicus Uncertainties in Ensembles of Regional Reanalyses (UERRA) dataset was used to obtain precipitation and volumetric soil moisture data. The results of this study indicated that precipitation information is still a much better predictor of landslides triggering compared to the reanalyzed (i.e., not very detailed) soil moisture data. This conclusion is valid both for local (i.e., grid) and regional (i.e., catchment-based) scales. Additionally, at the regional scale, soil moisture data can only predict a few landslide events (i.e., on average around one) that are not otherwise predicted by the simple empirical rainfall threshold approach; however, this approach on average, predicted around 18 events (i.e., 55% of all events). Despite this, additional investigation is needed using other (more complete) landslide databases and other (more detailed) soil moisture products.

Satellite rainfall products outperform ground observations for landslide prediction in India

Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries, where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important detection and monitoring process to predict landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide prediction in reducing the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide prediction in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides that occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (0.1∘ vs. 0.25∘) and temporal (hourly vs. daily) resolutions. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g. overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

Forecasting Landslides Using Mobility Functions: A Case Study from Idukki District, India

Catastrophic landslides and associated destructions are increasing every year, because of the change in climatic conditions and land-use patterns. The ecologically sensitive zones of Western Ghats are highly susceptible to landslides and require scientific attention in developing an efficient early warning system. Definition of empirical rainfall thresholds on local, regional or global scales is the most commonly followed method of forecasting rainfall induced landslides. The limitations associated with such thresholds demands for better forecasting performance, incorporating the effect of physical processes in the initiation of landslides. This study is an attempt to forecast landslides in Idukki district, using mobility functions. The function separates the impossible and certain mobilisation parts and forecasts whether landslides can occur or not. Based on the critical value of mobility function, two different warning levels are proposed for four different reference areas in the district. The study shows that the model is 97% efficient in smaller areas with uniform topographical and geological conditions, and the performance is reduced as the area becomes larger, with varying topographical and geological properties. The model proves to be an effective landslide forecasting tool that can be integrated with a rainfall forecasting system, to develop an early warning system for the region.

Changes in the rainfall event characteristics above the empirical global rainfall thresholds for landslide initiation at the pan-European level

Rainfall-induced landslides are one of the most frequent natural disasters. Detection of changes in the characteristics of the rainfall events that can be regarded as triggering factor is therefore of primary importance for policy-makers, practitioners, civil protection units and others. This study evaluated changes in the frequency and intensity of rainfall events above selected empirical rainfall thresholds at the pan-European level. For areas classified as at least moderately susceptible to landslides according to the ELSUS v2 map (i.e. around 1.8 * 106 km2; around 37% of the area), changes were evaluated for the 1961–2018 period. The regional rainfall reanalysis data was used as input. Four different empirical rainfall thresholds were selected and changes in the frequency of rainfall events above the thresholds and their characteristics were analysed. The results showed that, on average, 14% more rainfall events above the threshold were recorded in the 1991–2018 period compared to 1961–1990 at the European level according to the selected empirical rainfall threshold. Moreover, several regions were found where frequency and intensity of rainfall events above the four selected global empirical rainfall thresholds were positive and statistically significant. These regions were for example Italy, Carpathian Mountains, British Isles and some other parts of Europe. Some of these areas are also regions where landslides susceptibility is high or even very high and where multiple landslides were recorded in the past. On the other hand, there are also regions where frequency and intensity of events above the threshold were negative and statistically significant. These regions were parts of Central Europe, Alps and Pyrenees. Also, in these regions landslides frequently occur. According to different empirical thresholds, no significant change in the frequency of rainfall events above the thresholds could be detected for around 50% of the investigated area.

Rainfall and weather conditions inducing intense landslide activity in northern Spain (Deba, Guipúzcoa)

ABSTRACT Deba area is intensely affected by frequent shallow landslides triggered by rainfall. This contribution explores the role of rainfall in landslide activity during a quite long time span (60 years), from a large network of rainfall gauges and a complete inventory of landslides. Out of 1,180 landslides inventoried, more than 50% occurred simultaneously in 6 known dates, corresponding to 6 episodes triggering multiple landslides; 3,241 rainfall episodes have been automatically recognized and characterized in terms of rainfall amount and duration, providing a representative dataset that covers a wide range of movement types and behaviors.The relationship between rainfall episodes driving multiple movements simultaneously has not been explored in depth so far in northern Spain. The extraordinary character of the triggering rainfall has been assessed and empirical rainfall thresholds (total amount, and mean intensity), producing multiple landslides, have been found and compared with others described in the literature. Also, the meteorological conditions associated to those extreme events have been recognized: multiple landslide occurrences are triggered by extreme convective rainfall: intense, short and with limited horizontal extent, as well as a marked summer-autumn seasonality. This weather pattern is more characteristic of Mediterranean areas than of mild marine west-coast climates.

Using Field-Based Monitoring to Enhance the Performance of Rainfall Thresholds for Landslide Warning

Landslides are natural disasters which can create major setbacks to the socioeconomic of a region. Destructive landslides may happen in a quick time, resulting in severe loss of lives and properties. Landslide Early Warning Systems (LEWS) can reduce the risk associated with landslides by providing enough time for the authorities and the public to take necessary decisions and actions. LEWS are usually based on statistical rainfall thresholds, but this approach is often associated to high false alarms rates. This manuscript discusses the development of an integrated approach, considering both rainfall thresholds and field monitoring data. The method was implemented in Kalimpong, a town in the Darjeeling Himalayas, India. In this work, a decisional algorithm is proposed using rainfall and real-time field monitoring data as inputs. The tilting angles measured using MicroElectroMechanical Systems (MEMS) tilt sensors were used to reduce the false alarms issued by the empirical rainfall thresholds. When critical conditions are exceeded for both components of the systems (rainfall thresholds and tiltmeters), authorities can issue an alert to the public regarding a possible slope failure. This approach was found effective in improving the performance of the conventional rainfall thresholds. We improved the efficiency of the model from 84% (model based solely on rainfall thresholds) to 92% (model with the integration of field monitoring data). This conceptual improvement in the rainfall thresholds enhances the performance of the system significantly and makes it a potential tool that can be used in LEWS for the study area.

Rainfall thresholds of shallow landslides in Wuyuan County of Jiangxi Province, China

Abstract Rainfall is a critical factor inducing landslides, and thus the study of rainfall thresholds is of great significance for the prediction and prevention of landslides. In June 2017, infrastructures such as electric power pylons and roads were threatened by group-occurring landslides due to continuous heavy rainfall in Wuyuan County, Jiangxi Province of China. Based on the analysis of the rainfall data from March to September in this region, the lower (92.4 mm/d) and the upper (217.1 mm/d) empirical rainfall thresholds were determined. The soil water characteristic parameters of a typical landslide were determined by laboratory tests and back-analysis. Then, the factor of safety (FOS) versus time and the mechanical response of failure process with rainfall infiltration were examined. The results showed that during rainfall infiltration, the pore-water pressure increased, while the matrix suction and the stability decreased gradually. After the rain, the FOS increased slowly to a constant value, which was smaller than the initial. The physical rainfall threshold (200 mm/d), determined using 18 numerical simulation tests considering different rainfall intensities and amounts, was consistent with the empirical rainfall threshold. The methods developed in this work provide a useful tool for the prediction of landslides under extreme rainfall conditions.

Physical Interpretation of Rainfall Thresholds for Runoff‐Generated Debris Flows

AbstractRainfall thresholds for the occurrence of debris flows are commonly defined by Intensity‐Duration curves (IDthresholds). Interestingly, many empiricalIDthresholds show up as straight lines in a log‐log plot and therefore can be expressed by power‐law functions of the formI = α ⋅ Dβ, whereαis the scaling coefficient andβis the exponent of the power function. The different values ofαandβreflect the variability of geological and hydrological conditions in the different areas. In most cases, however, field conditions are so complex that a quantitative interpretation of the empirical rainfall threshold is impossible and the physical meaning ofαandβremains obscure. In this work, we provide a physical interpretation of the rainfall thresholds that characterize an active debris flow catchment in the Eastern Italian Alps (the Dimai Basin, Belluno Province). The catchment is affected by frequent debris flows generated by surface‐water runoff and has been monitored since 2010 to investigate the initiation process. Monitoring data allowed for the detection of two rainfall thresholds: a lower one that identifies the arrival of water in the initiation area (Catchment Outflow Threshold) and an upper one that identifies the initiation of debris flows by channel runoff (Debris Flow Threshold). We demonstrate that these two thresholds can be satisfactorily reproduced by a simple physically based model in which the excess rainfall is routed over the catchment using a kinematic‐wave scheme. This simple analysis provides a sound explanation of the observed thresholds and can be used to develop physically based thresholds for runoff‐generated debris flows.

Determination of Empirical Rainfall Thresholds for Shallow Landslides in Slovenia Using an Automatic Tool

Rainfall-triggered shallow landslides represent a major threat to people and infrastructure worldwide. Predicting the possibility of a landslide occurrence accurately means understanding the trigger mechanisms adequately. Rainfall is the main cause of slope failures in Slovenia, and rainfall thresholds are among the most-used tools to predict the possible occurrence of rainfall-triggered landslides. The recent validation of the prototype landslide early system in Slovenia highlighted the need to define new reliable rainfall thresholds. In this study, several empirical thresholds are determined using an automatic tool. The thresholds are represented by a power law curve that links the cumulated event rainfall (E, in mm) with the duration of the rainfall event (D, in h). By eliminating all subjective criteria thanks to the automated calculation, thresholds at diverse non-exceedance probabilities are defined and validated, and the uncertainties associated with their parameters are estimated. Additional thresholds are also calculated for two different environmental classifications. The first classification is based on mean annual rainfall (MAR) with the national territory divided into three classes. The area with the highest MAR has the highest thresholds, which indicates a likely adaptation of the landscape to higher amounts of rainfall. The second classification is based on four lithological units. Two-thirds of the considered landslides occur in the unit of any type of clastic sedimentary rocks, which proves an influence of the lithology on the occurrence of shallow landslides. Sedimentary rocks that are prone to weathering have the lowest thresholds, while magmatic and metamorphic rocks have the highest thresholds. Thresholds obtained for both classifications are far less reliable due to the low number of empirical points and can only be used as indicators of rainfall conditions for each of the classes. Finally, the new national thresholds for Slovenia are also compared with other regional, national, and global thresholds. The thresholds can be used to define probabilistic schemes aiming at the operative prediction of rainfall-induced shallow landslides in Slovenia, in the framework of the Slovenian prototype early warning system.

The Selection of Rain Gauges and Rainfall Parameters in Estimating Intensity-Duration Thresholds for Landslide Occurrence: Case Study from Wayanad (India)

Recurring landslides in the Western Ghats have become an important concern for authorities, considering the recent disasters that occurred during the 2018 and 2019 monsoons. Wayanad is one of the highly affected districts in Kerala State (India), where landslides have become a threat to lives and properties. Rainfall is the major factor which triggers landslides in this region, and hence, an early warning system could be developed based on empirical rainfall thresholds considering the relationship between rainfall events and their potential to initiate landslides. As an initial step in achieving this goal, a detailed study was conducted to develop a regional scale rainfall threshold for the area using intensity and duration conditions, using the landslides that occurred during the years from 2010 to 2018. Detailed analyses were conducted in order to select the most effective method for choosing a reference rain gauge and rainfall event associated with the occurrence of landslides. The study ponders the effect of the selection of rainfall parameters for this data-sparse region by considering four different approaches. First, a regional scale threshold was defined using the nearest rain gauge. The second approach was achieved by selecting the most extreme rainfall event recorded in the area, irrespective of the location of landslide and rain gauge. Third, the classical definition of intensity was modified from average intensity to peak daily intensity measured by the nearest rain gauge. In the last approach, four different local scale thresholds were defined, exploring the possibility of developing a threshold for a uniform meteo-hydro-geological condition instead of merging the data and developing a regional scale threshold. All developed thresholds were then validated and empirically compared to find the best suited approach for the study area. From the analysis, it was observed that the approach selecting the rain gauge based on the most extreme rainfall parameters performed better than the other approaches. The results are useful in understanding the sensitivity of Intensity–Duration threshold models to some boundary conditions such as rain gauge selection, the intensity definition and the strategy of subdividing the area into independent alert zones. The results were discussed with perspective on a future application in a regional scale Landslide Early Warning System (LEWS) and on further improvements needed for this objective.