Landslide Early Warning System Research Articles

A Regional-Scale Landslide Early Warning System Based on the Sequential Evaluation Method: Development and Performance Analysis

A regional-scale landslide early warning system was developed in collaboration with a city government. The structure and distinctive features of the system are described in detail. This system employs the principles of the sequential evaluation method that consecutively applies three different evaluation stages: statistical, physically based, and geomorphological evaluations. Based on this method, the system determines five phases of warning levels with improved levels of certainty and credibility. In particular, the warning levels are systematically derived to enable the discrimination of slope failures and debris flows. To provide intuitive and pragmatic information regarding the warning capabilities of the system, a comprehensive performance analysis was conducted. Early warning level maps were generated and a historical landslide database was established for the study period from 2009 to 2016. As a result, 81% of historical slope failures and 86% of historical debris flows were correctly predicted by high-class warning levels. Miscellaneous details associated to the timing efficiency of warnings were also investigated. Most notably, five high-class warning level events and four landslide events were recorded for a study region during the eight-year period. The four landslide events were all successfully captured by four out of the five warning events.

Enhancing the reliability of landslide early warning systems by machine learning

This paper submits a report on the effective adoption of machine learning algorithms for enhancing the reliability of rainfall-induced landslides. The challenges involved in the design of reliable landslide early warning systems (LEWS) and the data-driven context for overcoming these challenges have been presented. The operation of LEWS is explained using the chain of five major components (i) Data collection, (ii) Data transmission, (iii) Modelling, analysis and forecasting, (iv) Warning, and (v) Response. Failure of any of these major components of the LEWS will break the chain of operation of LEWS and the ensued consequences of each component failure are reviewed. Inferences drawn from the analysis of the reliability measures incorporated in 12 LEWS deployments across a dozen locations around the world are also presented. Based on the investigations from 12 LEWS and the real-world experience, we identified that an alternate solution is required for ensuring the reliability of LEWS, especially during disaster scenarios when warnings are crucial, but data availability is a constraint. We recognized that machine learning algorithms can provide an alternate solution and in this paper, we have discussed two machine learning approaches nowcasting and forecasting for enhancing the reliability. Both the algorithms employ historic data of the landslide monitoring parameters to learn the changes materializing in slope leading to landslide incidences. The learned knowledge is used to nowcast and forecast the real-time and future conditions of the slope from the real-time landslide monitoring parameters. In terms of ensuring reliability, (i) Nowcasting algorithm provides an alternate solution if either the Data collection component or Data transmission component of a LEWS fails. (ii) Forecasting algorithm provides extra lead-time for early warning and solves the problem of less lead-time during early warning process. The breakthrough is even when the real-time landslide monitoring parameters are not available for various reasons, these algorithms take the minimal input of rainfall forecast information for nowcasting and forecasting thus restoring the broken chain of operation of LEWS.

Estimation of rainfall thresholds for shallow landslides in the Sierra Madre Oriental, northeastern Mexico

Landslides induced by prolonged rainfalls are frequent mass movements along the northeastern portion of the Sierra Madre Oriental in Mexico, causing significant damage to infrastructure. In this work, we have studied the connection between rainfall and landslides in the Santa Rosa Canyon, a catchment located in the northeastern Mexico. A landslide database triggered by major storms and hurricanes that have hit the region over the past 30 years was analyzed. A total of 92 rainfall events in the Santa Rosa Canyon were studied to determine the amount of precipitation needed to trigger shallow landslides. For each event the duration (D, in hours) and the cumulated rainfall event (E, in mm) were determined by using historical rainfall data from weather stations located near the study area. We have proposed an ED threshold for rainfall-induced landslides with durations 0.5 < D < 120 hours to address the conditions that trigger these events in the study area. On analyzing the obtained threshold, it has been established that almost 60 mm of a daily rainfall accumulation is required to trigger shallow landslides in the study area. This estimation is consistent with other calculations made for areas close to the Santa Rosa Canyon. Finally, we validated the predictive capability of the threshold with a different set of rainfall data that did not result in landslides performing a straightforward receiver operating characteristic analysis. A good approach was obtained, especially for rainfall events with daily measurements. Results could be used as input information in the design of a landslide early warning system for the northeastern Mexico, and replicated for other landslide prone areas in the region.

Estimation of Risk Thresholds for a Landslide in the Three Gorges Reservoir Based on a KDE-Copula-VaR Approach

The Three Gorges Reservoir area, one of the most landslide-prone areas in China, is characterized by widely distributed deep-seated landslides exhibiting creep deformation due to rainfall and reservoir fluctuation. Thresholds, which are a key component for a reliable landslide early warning system, are still lacking for the prediction of movements of deep-seated reservoir landslides with creep deformation because information about reservoir fluctuation indicators is lacking, uncertainty is ignored, and binary output is provided. The risk threshold, defined as the tolerance criteria for risks that will lead to action, is an effective measure of the degree of uncertainty. In the present study, a hybrid approach utilizing kernel density estimation, a copula function, and the value at risk is proposed for the estimation of the risk threshold for the Baishuihe Landslide, a typical deep-seated landslide in the Three Gorges Reservoir area. Historical observations over approximately 15 years including rainfall, reservoir fluctuation, and landslide velocity were used to extract the risk threshold. A three-level risk threshold describing the minimum magnitudes of rainfall and reservoir fluctuation for changing the landslide movement state under three confidence levels was developed. A three-level risk response procedure, including risk responses in yellow alert, orange alert, and red alert, is proposed for risk management. Given its successful application, the present approach can be used to estimate the risk threshold for deep-seated landslides.

Influence of the mapping unit for regional landslide early warning systems: comparison between pixels and polygons in Catalonia (NE Spain)

This work presents a prototype landslide early warning system (LEWS) adapted to real-time performance over the region of Catalonia (NE Spain). The system uses high-resolution rainfall information obtained from weather radar observations and susceptibility maps to issue a qualitative warning level at a regional scale. To study the influence of the mapping unit on the LEWS outputs, susceptibility maps obtained for Catalonia based on (i) pixels of different sizes and (ii) hydrological subbasins have been compared. The susceptibility has been derived using a simple fuzzy logic approach combining slope angle and land cover data. The susceptibility maps for the different mapping units have then been employed to run the LEWS for a period of 7 months (warm season of 2010). For each configuration, the performance, interpretability of the warnings, and computational requirements have been compared to assess the suitability of each mapping unit for their use in the LEWS in real time. The configuration using pixels of 30-m resolution as mapping units seems to be the best as a compromise between resolution, performance, and computational cost. However, from an end-user’s real-time perspective, the interpretation of the warnings can be difficult. Therefore, summarizing and visualizing the warnings, which are computed over the high-resolution grid, by subbasins is proposed as the best option.

Evaluasi Penerapan Sistem Peringatan Dini Bencana Longsor di Kampung Jatiradio, Desa Cililin, Kecamatan Cililin, Kabupaten Bandung Barat

Indonesian territory is a disaster-prone region. There are at least 12 disaster threats that are grouped in geological disasters (earthquakes, tsunamis, volcanoes, landslides), hydrometeorological disasters (floods, flash floods, droughts, extreme weather, extreme waves, forest and land fires), and anthropogenic disasters (epidemics of disease outbreaks and failed technology-industrial accidents). Landslides or land movements in Indonesia in recent years have increased in intensity and frequency. One of the efforts to reduce the risk of landslides that has been carried out by the Disaster Risk Reduction Technology Center-BPPT is to build an Landslide Early Warning System in Jatiradio Village, Cililin District, West Bandung Regency. In order to uniform the implementation of an early warning system for landslides or land movements in disaster prone areas, the government has established a national standard namely SNI 8235: 2017 on the Early Warning System for Land Movements. The purpose of this study is to evaluate the compliance between the Landslide Early Warning System that has been developed by PTRRB-BPPT with the Indonesian National Standard for the Land Movement Early Warning System (SNI 8235: 2017). Based on the results of data analysis and evaluation, it can be concluded that the implementation of the landslide early warning system in Jatiradio Village, Cililin Village, Cililin District, West Bandung Regency is in accordance with the Indonesian National Standard (SNI 8235: 2017) concerning the Soil Movement Early Warning System.

Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach

Empirical thresholds indicating the meteorological conditions leading to shallow landslide triggering are one of the most important components of landslide early warning systems (LEWS). Thresholds have been determined for many parts of the globe and present significant margins of improvement, especially for the high number of false alarms they produce. The use of soil moisture information to define hydro-meteorological thresholds is a potential way of improvement. Such information is becoming increasingly available from remote sensing and sensor networks, but to date, there is a lack of studies that quantify the possible improvement of the performance of LEWS. In this study, we investigate this issue by modelling the response of slopes to precipitations, introducing also the possible influence of uncertainty in soil moisture provided by either field sensors or remote sensing, and investigating various soil depths at which the information may be available. Results show that soil moisture information introduced within hydro-meteorological thresholds can significantly reduce the false alarm ratio of LEWS, while keeping at least unvaried the number of missed alarms. The degree of improvement is particularly significant in the case of soils with small water storage capacity.

Efforts of Establishing Disaster Resilient Villages through Kuliah Kerja Nyata Tematik at Disaster Risk Reduction in Purbalingga District

Desa tangguh bencana (Destana) is a program aimed to make a village community prepared for and resilient to disasters. The Destana program was initiated by the National Disaster Management Agency (BNPB) decreed by the Regulation of the head of BNPB No. 1/2012. A village represents the bottom line in the government system and therefore the capacity building for disaster resilience starts from the villages. The number of villages in Central Java reaches 8559 villages, while BNPB, BPBD Province, BPBD Regency/City can only launch Destana on average 30 villages each year from 2013 - 2018. As a result, it takes quite a long time to forming Destana in all villages in Central Java. Therefore, the role of universities, in particular the state universities in forming Destana, is really needed. One of the activities in the formation of Destana was in the kuliah kerja nyata tematik program (KKN PRB) conducted in Purbalingga Regency in 2 villages that were prone to landslides. Provisioning for students refers to Perka BNPB Number 1/2012 by implementing 20 Destana indicators. Gunungwuled village and Panusupan village were the two villages where the KKN PRB done by 10 students each. For 45 days students could build several indicators, i.e. making disaster risk maps calculated based on hazard maps, vulnerability maps, and capacity maps. Next, they made a Landslide Contingency Planning Document with residents and relevant stakeholders. The Village Disaster Risk Reduction Forum consisting of various community components and the BPBD of Purbalingga Regency was also formed. Afterwards, the capacity building activities to the community against disasters were conducted. Finally, an early warning system for landslides in areas with potential landslides was created and warning signs for landslide-prone areas and evacuation route signs were installed. With the help of KKN PRB, the Panusupan village and Gunungwuled village have now become disaster resilient villages.

IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas.

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017–2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region.

Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

Assessing the potential of soil moisture measurements for regional landslide early warning

In mountainous terrain, rainfall-induced landslides pose a serious risk to people and infrastructure. Regional landslide early warning systems (LEWS) have proven to be a cost-efficient tool to inform the public about the imminent landslide danger. While most operational LEWS are based on rainfall exceedance thresholds only, recent studies have demonstrated an improvement of the forecast quality after the inclusion of soil hydrological information. In this study, the potential of in situ soil moisture measurements for regional landslide early warning is assessed. For the first time, a comprehensive soil moisture measurement database was compiled for Switzerland and compared with a national landslide database (Swiss flood and landslide damage database, WSL). The time series were homogenized and normalized to represent saturation values. From ensembles of sensors, the mean and standard deviation saturation were calculated and infiltration events were delimited, characterized, and classified as landslide-triggering or non-triggering based on the occurrence of landslides within a specified forecast distance. A logistic regression function was applied to model the landslide activity based on the infiltration event characteristics and several models were analysed and compared with receiver operating characteristics (ROC). A strong distance dependence becomes apparent showing a forecast goodness decrease with increasing distance between water content measurement site and landslide, and a better forecast goodness for long-lasting as opposed to short-duration precipitation events. While most variability can be explained by the two event properties antecedent saturation and change of saturation during an infiltration event, event properties that describe antecedent conditions are more important for long-lasting as opposed to short-duration precipitation events that can be better explained by properties describing event dynamics. Overall, the analysis demonstrated that in situ soil moisture data effectively contains specific information useful for landslide early warning.

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.

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 (&lt;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.

Application of geospatial technologies in developing a dynamic landslide early warning system in a humanitarian context: the Rohingya refugee crisis in Cox’s Bazar, Bangladesh

Since August 2017, more than 744,400 stateless Rohingya refugees – an ethnic Muslim minority group from the Rakhine State – have entered Bangladesh to escape serious crimes against humanity conducted by the Myanmar Army. Extensive level of deforestation and hill cutting activities took place in Cox’s Bazar District (CBD) in Bangladesh to accommodate them. The refugee camps are sitting on hills and loose soil and are highly vulnerable to rainfall-triggered landslides. Notably in June 2017, landslides in the same region killed at least 160 people. From this perspective, the study aims to develop a localised landslide early warning system (EWS) for the Rohingya refugees and their host communities in CBD. A novel method, combining landslide inventory and susceptibility maps, rainfall thresholds and dynamic web-based alert system, has been introduced to develop the landslide early warning system (EWS) by applying advanced geoinformation techniques. Results suggest that approximately 5,800 hectares of forest land cover disappeared due to the 2017 Rohingya influx. Land cover changes through hill cutting andslope modifications, and unplanned urbanisation are predominantly responsible for slope failures and consecutive 5-day periods of rainfall between 95–220 mm could initiate landslides in high susceptible areas. The EWS would support the local authorities and international organisations in reducing disaster risks and saving lives from landslides in a humanitarian context.

Development of community-based landslide early warning system in the earthquake-affected areas of Nepal Himalaya

In the central Nepal Himalaya, landslides form the major natural hazards annually resulting in many casualties and damage. Structural as well as non-structural measures are in place to minimize the risk of landslide hazard. To reduce the landslide risk, a Landslide Early Warning System (LEWS) as a non-structural measure has been piloted at Sundrawati village (Kalinchowk rural municipality, Dolakha district) to identify its effectiveness. Intensive discussions with stakeholders, aided by landslide susceptibility map, resulted in a better understanding of surface dynamics and the relationship between rainfall and surface movement. This led to the development of a LEWS comprised of extensometers, soil moisture sensors, rain gauge stations, and solar panels as an energy source that blows siren receiving signals via a micro-controller and interfacing circuit. The data generated through the system is transmitted via a Global System for Mobile Communications (GSM) network to responsible organizations in real-time to circulate the warning to local residents. This LEWS is user-friendly and can be easily operated by a community. The successful pilot early warning system has saved 495 people from 117 households in August 2018. However, landslide monitoring and dissemination of warning information remains a complex process where technical and communications skill should work closely together.

Development of a Landslide Early Warning System in Indonesia

Landslides are one of the most disastrous natural hazards in Indonesia, in terms of number of fatalities and economic losses. Therefore, Balai Litbang Sabo (BLS) has developed a Landslide Early Warning System (LEWS) for Indonesia, based on a Delft–FEWS (Flood Early Warning System) platform. This system utilizes daily precipitation data, a rainfall threshold method, and a Transient Rainfall Infiltration and Grid-based Regional Slope-stability model (TRIGRS) to predict landslide occurrences. For precipitation data, we use a combination of 1-day and 3-day cumulative observed and forecasted precipitation data, obtained from the Tropical Rainfall Measuring Mission (TRMM) and the Indonesian Meteorological Climatological and Geophysical Agency (BMKG). The TRIGRS model is used to simulate the slope stability in regions that are predicted to have a high probability of landslide occurrence. Our results show that the landslides, which occurred in Pacitan (28 November 2017) and Brebes regions (22 February 2018), could be detected by the LEWS from one to three days in advance. The TRIGRS model supports the warning signals issued by the LEWS, with a simulated factor of safety values lower than 1 in these locations. The ability of the Indonesian LEWS to detect landslide occurrences in Pacitan and Brebes indicates that the LEWS shows good potential to detect landslide occurrences a few days in advance. However, this system is still undergoing further developments for better landslide prediction.

Estimation of Rainfall-Induced Landslides Using the TRIGRS Model

Rainfall-induced landslides have become the biggest threat in the Indian Himalayas and their increasing frequency has led to serious calamities. Several models have been built using various rainfall characteristics to determine the minimum rainfall amount for landslide occurrences. The utilisation of such models depends on the quality of available landslide and rainfall data. However, these models do not consider the effect of local soil, geology, hydrology and topography, which varies spatially. This study is to analyse the triggering process for shallow landslides using physical-based models for the Indian Himalayan region. This research focuses on the utilisation and dependability of physical models in the Kalimpong area of Darjeeling Himalayas, India. The approach utilised the transient rainfall infiltration and grid-based regional slope-stability (TRIGRS) model, which is a widely used model in assessing the variations in pore water pressure and determining the change in the factor of safety. TRIGRS uses an infinite slope model to calculate the change in the factor of safety for every pixel. Moreover, TRIGRS is used to compare historical rainfall scenarios with available landslide database. This study selected the rainfall event from 30th June to 1st July 2015 as input for calibration because the amount of rainfall in this period was higher than the monthly average and caused 18 landslides. TRIGRS depicted variations in the factor of safety with duration before, during and after the heavy rainfall event in 2015. This study further analysed the landslide event and evaluated the predictive capability using receiver operating characteristics. The model was able to successfully predict 71.65% of stable pixels after the landslide event, however, the availability of more datasets such as hourly rainfall, accurate time of landslide event would further improve the results. The results from this study could be replicated and used in other unstable Indian Himalayan regions to establish an operational landslide early warning system.

Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach

Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D−0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region.

CAN GEOINFORMATION HELP TO BETTER PROTECT INFORMAL SETTLEMENTS? - A CONCEPT FOR THE CITY OF MEDELLIN

Abstract. New contributions to disaster research need to address the increasing vulnerability of informal settlements in a changing climate situation. Informal settlements are frequently built in hazardous areas and are often left out of traditional disaster risk management concepts. Hence, formal and informal societal structures, as well as technical systems to warn against, handle or mitigate natural hazards, need to evolve. Within the project Inform@Risk we are addressing these issues based on a case study in Medellín (Colombia). Here, as a result of civil conflicts informal dwellings were partly constructed by people displaced from rural areas. They are mainly located in the urban peripheral areas along steep and unstable slopes, where the resettlement of all inhabitants at risk of landslides is unfeasible. This contribution presents the technical infrastructure and the concept to incorporate geodata from different sources in an integrated landslide early warning system for some selected informal settlements of Medellin. Special attention is given to possibilities on how building societal institutions, supported by information systems, increases local resilience. Using geoformation as a basis, we will combine classical participatory planning methods with digitally assisted concepts. These include combining satellite and UAS based remote sensing data with terrestrial sensor networks, crowd sourcing and citizen science to collect volunteered geographic information about the settlement and its environmental parameters, as well as distribute this information and disseminate warnings to the local population.

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.