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Landslide is a common natural disaster occurring in Indonesia during the rainy season from November to February. Attempts have been made to develop an early warning system based on the rainfall derived from satellite observation. It is essential to verify the accuracy level of the rainfall threshold in predicting the occurrence of rainfall, causing landslides and non-landslides to model the lower limit that can be used as an early warning device of the landslides. In this analysis, modelling was carried out with an empirical (intensity—duration/ID) approach using 220 data of rainfall that triggered landslide with satellite-based TRMM in Indonesia territory. The intensity and duration of antecedent rainfall were utilized in rainfall threshold modelling. The rainfall threshold was validated with ROC analysis. This method used seven statistics indices and ROC curve to determine the accuracy rate of the rainfall threshold. The results showed empirical equation I = 7.83D−0.328 within the interval time 2–18 days. The results of the analysis of the ROC on the rainfall threshold indicate that the model has a good accuracy rate and can be used in an early warning system of landslide even though it still has a fairly high error rate.

This paper gives an overview of recent research on generating landslide inventories of triggering events by earthquake and extreme rainfall in the Himalayan context, and how these influence subsequent landslide susceptibility, hazard and risk assessment. Within a collaboration project between between ITC, GSI and NRSC , a number of techniques were developed for landslide inventory mapping, ranging from using Object Based Image Analysis, to the use of available records and collaborative mapping using Google Earth Images for the generation of event-based landslide inventories. The completeness of landslide inventories and its spatial and thematic accuracy are discussed in relation to the effect they have on the evaluation of landslide susceptibility and hazard assessment. Also a collaboration with the USGS is discussed for the generation of a web-based portal of earthquake-induced landslide inventories for major historical earthquakes. It is very important to collect as many of these event-based inventories in order to better correlate them with the specific characteristics of the earthquake that triggered them and the terrain conditions, in order to develop methods for earthquake-induced landslide hazard. Event based landslide inventories are used to estimate the relation between temporal probability, landslide density and landslide size distribution. Examples are given of the work that was carried out after the 2015 Gorkha earthquake in Nepal. Object Based Image Analysis (OBIA) combined with machine learning methods were used to generate landslide inventories from before, during and after the earthquake, and the characteristics of earthquake-induced landslide inventories were compared with rainfall-induced landslide inventories. The contributing factors related to topography, geological and land cover were analyzed for both inventories for different landslide types and sizes. In order to develop rainfall thresholds as a basis for Landslide Early Warning a study was carried out in the Rasuwa district of Nepal, using a combination of satellite based rainfall estimates, rainfall station data, and physically-based modelling of soil water conditions and slope stability. Also multi-hazard relationships (earthquake-landslides-flooding-debrisflows) were modelled using a physically-based model in comparable environments affected by earthquakes, in order to analyze the post-earthquake changes in landslide hazard intensity.

The Academy of ICT Essentials for Government Leaders module series has been developed by the Asian and Pacific Training Centre for Information and Communication Technology for Development (APCICT). This module introduces disaster risk management (DRM) and provides an overview of how information and communication technologies (ICTs) can be used for DRM. A large number of examples and case studies on the applications of ICTs in DRM have been included in the module. The main objective of the module is to introduce the basic concepts of DRM and the applications of ICTs in disaster mitigation and prevention, preparedness, response, and recovery. The target audiences of this module are policymakers and civil servants at the national and local government levels who are responsible for or engaged in DRM activities.

Floods and associated landslides account for a large number of natural disasters and affect many people wherever they occur. Sediment-free floods are rare, and in most cases, floods carry notable amount of sediments. Mass movement processes also transport a huge amount of sediments within a short period. The mobilized sediments cause significant costs and damages as soon as they reach urban or rural environments. These damages and costs include (but are not limited to) cleaning or dredging cost, damage to contents of buildings (e.g. furniture, electric appliances, etc.), and blockage of drainage and sewer systems which get filled up with sediments. Dominica is significantly vulnerable to tropical storms and hurricanes. It is a mountainous island covered by tropical rainforests and located about halfway between the French islands of Guadeloupe and Martinique in the Eastern Caribbean sea. Hurricane Maria made landfall on this island on September 18th, 2017 and it heavily impacted the housing, transport infrastructure, tourism, agriculture, and education sectors. The intense rainfall caused flash floods, landslides, and debris flows resulting in a massive amount of sediments being deposited in urban and rural areas. The overall damages and losses are estimated at approximately USD 1.3 billion. Dominica’s Ministry of Public Works reported that the total cost related to deposition of sediments (e.g. dredging rivers, cleaning streets and main roads, and clearing of airports and seaports) exceeds USD 92 million which is a considerable portion of total damages and costs. This implies the significance of the risk imposed by sediment deposition. This study aims to achieve a reliable sediment deposition quantification which is useful for assessing the risk of such events. Three methods were proposed for this purpose. First, the sediment deposition height was determined in the field. Second, deposition surface was simulated using trend interpolation and DEM was subtracted from that to get deposition height. Third, the deposition height and extent were determined by calculating the difference in elevation using pre- and post-event drone and LiDAR flights. The results were compared with each other and the findings of in-situ investigations of deposition. They indicate similar deposition heights and volumes, however, the pattern and extent of deposition are not the same. The practicality of the third method depends on the availability of data, but when data is available its outcomes provide a reliable assessment of sediment deposition volume. However, this cannot be trusted unless an in-situ investigation is performed.

The aim of this paper is to present the first results of a research project entitled: Strengthening Local Authorities in Risk Management (SLARIM). The main objective of this project is to develop a methodology for spatial information systems for municipalities, which will allow local authorities to evaluate the risk of natural disasters in their municipality, in order to implement strategies for vulnerability reduction. The project concentrates on medium-sized cities in developing countries, which do not yet utilize Geographic Information Systems in their urban planning, and which are threatened by natural hazards (such as earthquakes, flooding, landslides and vol-canoes). The methodology concentrates on the application of methods for hazard assessment, elements at risk mapping, vulnerability assessment, risk assessment, and the development of GIS-based risk scenarios for varying hazard scenarios and vulnerability reduction options, using structural and/or non-structural measures. In the development of elements at risk databases use is made of interpretation of high-resolution satellite imagery, combined with extensive field data collection, using mobile GIS. Although the methodology is primarily designed to assist municipalities in the decision-making regarding vulnerability reduction strategies, the resulting databases are designed in such a way that they can also be utilized for other municipal activities. Within the project a number of case study cities have been identified. Here results are presented on earthquake loss estimation for the city of Lalitpur in Nepal, for buildings and for population losses. Databases have been generated of the buildings of the city, and of the sub-surface conditions. Soil response modeling was carried out and vulnerability curves are applied to estimate the losses for different earthquake scenarios.

Landslide inventory in Nepal is currently not done continuously and coherently, and efforts are made after major triggering events only, such as the 2015 Gorkha earthquake. There is a lack of sharing of knowledge and cooperation among stakeholders to cope with significant disaster events. This research will focus on filling the gaps by designing an approach for sustainable landslide inventory mapping in Nepal. The overall framework of research is to make a conceptual design of a Nepalese landslide information system and to show how different stakeholders can be involved. The use of a Nepalese landslide information system, to report and provide landslide data would benefit the stakeholders involved in data collection and landslide management. Several stakeholders could be considered such as road department, land management authority, forestry department, disaster management department and army. These stakeholders have different data requirements and are determined in this research. The landslide database system should be designed in such a way that it can incorporate information about different landslide characteristics and types. Availability and extraction of landslide data from the database are the critical aspects. The landslide characteristics to be comprised in the database are defined, such as the completeness, the form of representation, the date of occurrence, the subdivision in scarp and accumulation areas, and the type of landslides. For the reporting of landslides directly in the system, a web portal is proposed that will be connected to the internet and the central database. Stakeholders who can contribute to the reporting of landslides are identified by field interviews and questionnaires. For example, primary users like school teachers can report a landslide event by pointing it out in the system as schools at the district level have the required infrastructure and internet facilities. Also, different methods for generating the inventory data are analysed in this research, and the applicability and efficiency of various collection methods are tested, including, image interpretation, the use of field-based Tablet PC, interviews and field survey. Interviews were carried out focusing on farmers and village residents and local police officers. Landslide inventories for Gorkha event were collected to see the spatial coverage of landslides in the country. Effects of different quality and completeness levels of landslide inventories, using tools such as Frequency Area Distribution and spatial overlap were carried out. Based on field investigations and literature reviews the optimal format of the database is determined for the Nepalese landslide information system.

Summary statistics derived from the frequency–area distribution (FAD) of inventories of triggered landslides allows for direct comparison of landslides triggered by one event (e.g. earthquake, rainstorm) with another. Such comparisons are vital to understand links between the landslide‐event and the environmental characteristics of the area affected. This could lead to methods for rapid estimation of landslide‐event magnitude, which in turn could lead to estimates of the total triggered landslide area. Previous studies proposed that the FAD of landslides follows an inverse power‐law, which provides the basis to model the size distribution of landslides and to estimate landslide‐event magnitude (mLS), which quantifies the severity of the event. In this study, we use a much larger collection of earthquake‐induced landslide (EQIL) inventories (n=45) than previous studies to show that size distributions are much more variable than previously assumed. We present an updated model and propose a method for estimating mLS and its uncertainty that better fits the observations and is more reproducible, robust, and consistent than existing methods. We validate our model by computing mLS for all of the inventories in our dataset and comparing that with the total landslide areas of the inventories. We show that our method is able to estimate the total landslide area of the events in this larger inventory dataset more successfully than the existing methods. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

This atlas provides basic information and overviews of the occurrence of co-seismic landslides, the subsequent rainstorm-induced debris flows, and the methods used for hazard and risk assessment in the Wenchuan-earthquake affected area. The atlas pages are illustrated with maps, photos and graphs, showing the results of the inventory and analysis of co-seismic landslide and post-earthquake debris flows. The atlas summarizes the changing characteristics related to post-seismic mass movement activity after the earthquake. The atlas highlights methods to assess the hazard and risk of debris flows for 145 catchments in Wenchuan County at different scales. The atlas also displays some examples of risk management such as engineering measures and early warning systems. This atlas contributes to post-earthquake disaster reduction planning, mitigation, and reconstruction works in seismically active mountainous areas.

Large scale disasters (e.g. earthquakes, tropical cyclones, and wildfires) cause wide-spread losses to buildings, damaging transportation, educational, health, industry, and agricultural infrastructure, leading to direct and indirect social and economic losses (EM_DAT,2018) . After years with large losses by earthquakes (e.g. 2008 China, 2010 Haiti, 2015 Nepal) 2017 was characterized by hurricanes and wildfires, causing a loss of 300 billion Euro (MunichRe, 2018). These events also have a large impact on the natural environment, and critically change the conditions related to vegetation, active processes and hydrology leading to new hazards or increased intensity and frequency of existing hazards (Figure 1). This aspect is often not fully considered in post-disaster reconstruction planning, leading to unfortunate new impacts and losses, and to what could be called “re-reconstruction planning”. Land-use planning is a critical action for Priority 4 of the Sendai framework “Enhancing disaster preparedness for effective response and to “Build Back Better” in recovery, rehabilitation and reconstruction” (UNISDR, 2015).

Ecosystem services can play an important role as measures for disaster risk reduction. At the same time it is important to find out where and how ecosystem-based disaster risk reduction really can make a difference. If we want to find out what will be the effect of alternative risk reduction measures, how ecosystem services can play a role in this context, and how they compare with other types of interventions, then there is a clear role for geo-information. Geographical information, such as obtained from spatial-temporal simulation modelling and spatial multi-criteria evaluation, is used for analyzing and monitoring what could be the effect of alternative development scenarios on the exposure to natural hazards, or of different combinations of engineered, ecosystem-based and other non-structural risk reduction measures. This helps to set management priorities and propose actions for risk reduction and risk-informed spatial planning. With the help of a spatial decision support system, the effect of risk reduction alternatives and their effect on risk reduction – now and in the future – can be analyzed and compared. This can support the selection of ‘best’ alternatives. The recently developed RiskChanges is presented, which is a web-based, open-source spatial decision support tool for the analysis of changing risk to natural hazards. It is envisaged that the use of the RiskChanges will support the provision of relevant geo-information about risk and changes in risk, and thus provides input for structured risk reduction-, disaster response-, and spatial development-planning.

Future trends in debris flow activity are constructed based on bias-corrected climate change projections using two meteorological proxies: daily precipitation and Convective Available Potential Energy (CAPE) combined with specific humidity for two Alpine areas. Along with a comparison between proxies, future number of days with debris flows are analyzed with respect to different regional and global climate models, Representative Concentration Pathways (RCPs), and area for quantile mapping. Two different base periods are also analyzed, as debris flows were observed on only 6 (17) days between 1950 and 1979, yet on 18 (49) days between 1980 and 2009 for Fella River, NE Italy (Barcelonnette, SE French Alps). For both areas, future climate projections vary between no change up to an increase of 6.0 % per decade in days with debris flow occurrences towards the end of 21st century. In Barcelonnette, the base period and proxy have a bigger impact on the future number of debris flow days than the climate model or RCP used. In Fella River, the base period, RCP, and proxy used define the future range. Therefore the selection of proxy, base period and downscaling technique should be carefully considered for future climate change impact studies concerning debris flow activity and associated fast-moving landslides.

This paper presents a prototype of an interactive web-GIS tool for risk analysis of natural hazards, in particular for floods and landslides, based on open-source geospatial software and technologies. The aim of the presented tool is to assist the experts (risk managers) in analysing the impacts and consequences of a certain hazard event in a considered region, providing an essential input to the decision-making process in the selection of risk management strategies by responsible authorities and decision makers. This tool is based on the Boundless (OpenGeo Suite) framework and its clientside environment for prototype development, and it is one of the main modules of a web-based collaborative decision support platform in risk management. Within this platform, the users can import necessary maps and information to analyze areas at risk. Based on provided information and parameters, loss scenarios (amount of damages and number of fatalities) of a hazard event are generated on the fly and visualized interactively within the web-GIS interface of the platform. The annualized risk is calculated based on the combination of resultant loss scenarios with different return periods of the hazard event. The application of this developed prototype is demonstrated using a regional data set from one of the case study sites, Fella River of northeastern Italy, of the Marie Curie ITN CHANGES project.

© Springer International Publishing Switzerland 2015. The devastating 2008 Wenchuan earthquake with a magnitude of Mw 7.9 was the largest seismic event in China in more than 50 years. It triggered numerous landslides over a broad area, some of which dammed rivers, posing severe threats to downstream settlements. The coseismic landslides are classified into rock/debris avalanches, debris flows, rock/debris slides and rock falls. A number of representative examples of each type and the corresponding landslide dam features were studied. According to dam composition material and sedimen-tological features, landslide dams were categorized into three types: dams mainly composed of large boulders and blocks; dams composed of unconsolidated fine debris; and dams with partly intact rock strata at the base topped by large boulders and blocks or soil with rock fragments, showing two-layered or three-layered depositional structure. This classification is linked to the typology of damming landslides and considered to be a preliminary indicator of dam stability. In addition, dam stability also largely depends on valley morphometry as well as landslide runout distance and mechanism. .

Landslide mitigation measures are used to reduce the risk affecting mountain communities. The quantitative estimation of the change or reduction in risk, after implementing mitigation measures, requires modeling of past events and the forward prediction of possible future occurences. However, the forward-prediction of landslide hazard is subjected to uncertainties due to the lack of knowledge on some key aspects like the possible source volume that can be triggered and model parameters that determine the landslide runout. In this study, a back-analysis of a debris flow event was carried out using MassMov2D to create a set of parameter ranges for forward-predicting runouts with mitigation measures. We approached the issue of uncertainty by systematically sampling parameters from wide ranges and running hundreds of different runout scenarios. Simulations from back-analysis were compared with the forward-predicted models to determine changes in the spread and intensity of debris flows affecting elements at risk (e.g. houses and roads). This study is a first step towards a quantitative risk assessment (QRA) being carried out within the EC FP-7 funded CHANGES network (Grant Agreement No. 263953).

Landslides can result in enormous casualties and huge economic losses in mountainous regions. In recent years, interest in landslide risk assessment has grown substantially. Landslide risk assessment aims to determine the expected degree of loss due to landslides and the expected number of fatalities, people injured, damage to property and disruption of economic activity. This session addresses the landslide risk assessment, to establish criteria for ranking risk posed by different types of mass movements, to quantify the impact that mass movements have on population, structures and infrastructures, and to enhance strategies for avoiding or mitigating landslide risk.

Earthquakes may trigger a series of multiple cascading geohazard phenomena. For example, coseismic landslides may block rivers and form landslide dams, which occur frequently in tectonically active mountains with narrow and steep valleys. The catastrophic release of water masses from landslide-impounded lakes is capable to produce outburst floods and debris flows, causing loss of lives, housing and infrastructure. Quantifying the probability of these cascading phenomena following a triggering event has been a main research challenge. This study creates a conceptual event tree model for hazard assessment of earthquake-induced landslide dams, with the involvement of many discussions amongst specialists in different fields. Event tree (ET) is a graphical, hierarchical and tree-like representation of possible events, which has been successfully applied in the volcano hazard assessment, but the application in landslide research is rather limited. We attempt to elaborate the event tree model by applying it in estimating the hazard of landslide dams induced by the Wenchuan earthquake. The model starts from a scenario-earthquake on a known possible active faults; the model then progressively assesses the susceptibility to coseismic landslides and landslide dams, and, finally, provides an estimate of dam-break flood hazard. According to the literature and our best understanding of the seismic hazard, we suggested or proposed possible methods to estimate the probabilities at successive nodes, the cascading events.

Statistical based landslide susceptibility models are widely used in medium to regional scale assessments. The two main inputs in these models are the landslide inventory of past events and the landslide caustative factor maps. In this study we assessed how the performance and prediction capability of the Weights-of-Evidence (WofE) susceptibility model is affected by the way we represent landslides in the pixel format, considering the entire polygon or only the landslide centroid. Influence of pixel density representing the landslide polygon was also taken into account. The second part of the research concidered the effect of the landslide model training and prediction sample sizes on the performance and prediction rates of the WofE model. Two case study areas were chosen to apply the representation and sampling tests: (1) the Fella River Basin (Eastern Italian Alps) containing debris flows and (2) the four times larger Buzau County (Romanian Carpathians) containing shallow landslides. Both areas are very different in terms of size, landslide types and geo-environmental factors, and were chosen in order to determine the applicability and flexibility of our analysis. Our results indicate that there is only a minor increase in performance and prediction when increasing the number of pixels to represent the entire landslide polygon. As the number of pixels increased from a single centroid to all pixels within the polygon, we found that the relative increase in pixels was similar within all classes (e.g. grass-land, forest, bare rock) of each thematic factor map like land-use or litholo gy. This indicated that the landslides have a similar size across the entire study area and is one of the causes of the lack of significant increase in model performance. The similari ty in performance and prediction rates for different landslide representation tests was in contrast to their respective susceptibility maps, which did show significant differences among each-other. This requires further analysis in future studies to determine which susceptibility map should be chosen for decision making. As for the sample size analysis, we have found that using 10 to 20% of all landslides to train the WofE model in both case studies is sufficient to predict the remaining 80 to 90% of the landslides. Modeling with more than 20% of the landslides causes a "plateau effect" in the performance and prediction rates. This indicate s that only a small percentage of all the landslides in an inventory are needed for good prediction results, making it also unnecessary to map every landslide in the area for a suffici ent performing landslide susceptibility analysis.

This introductory chapter discusses key issues related to aspects of hazards and risks of natural processes in Mountain area's and discusses the framework of risk governance, which aims to integrate these elements.