Multi-hazard risk assessment using GIS in urban areas: a case study for the city of Turrialba, Costa Rica

In the framework of the UNESCO sponsored project on “Capacity Building for Natural Disaster Reduction” a case study was carried out on multi-hazard risk assessment of the city of Turrialba, located in the central part of Costa Rica. The city with a population of 33,000 people is located in an area, which is regularly affected by flooding, landslides and earthquakes. In order to assist the local emergency commission and the municipality, a pilot study was carried out in the development of a GIS –based system for risk assessment and management. The work was made using an orthophoto as basis, on which all buildings, land parcels and roads, within the city and its direct surroundings were digitized, resulting in a digital parcel map, for which a number of hazard and vulnerability attributes were collected in the field. Based on historical information a GIS database was generated, which was used to generate flood depth maps for different return periods. For determining the seismic hazard a modified version of the Radius approach was used and the landslide hazard was determined based on the historical landslide inventory and a number of factor maps, using a statistical approach. The cadastral database of the city was used, in combination with the various hazard maps for different return periods to generate vulnerability maps for the city. In order to determine cost of the elements at risk, differentiation was made between the costs of the constructions and the costs of the contents of the buildings. The cost maps were combined with the vulnerability maps and the hazard maps per hazard type for the different return periods, in order to obtain graphs of probability versus potential damage. The resulting database can be a tool for local authorities to determine the effect of certain mitigation measures, for which a cost-benefit analysis can be carried out. The database also serves as an important tool in the disaster preparedness phase of disaster management at the municipal level

Landslide hazard spatiotemporal prediction based on data-driven models:Estimating where, when and how large landslide may be

The geoscientific community primarily focuses on predicting where landslides are likely to occur through data-driven susceptibility models. Recently, few researchers have turned to statistical estimation of landslide planimetric area within a given terrain unit and exploration of the spatiotemporal distribution of landslide occurrence. However, these data-driven approaches cannot fulfill the commonly accepted definition of landslide hazard and cannot predict the location, time/frequency, and magnitude of landslide occurrence simultaneously. This study proposes a unified and data-driven framework for landslide hazard spatiotemporal modeling, enabling dynamic and probabilistic estimation of landslide occurrence within a given slope unit at a specific time period and for a specific landslide magnitude. This framework not only involves static and dynamic factors in modelling, but also considers spatial and temporal interactions to explore the spatiotemporal variation effects of landslide hazard. We test this framework on the main island of Taiwan with a multi-temporal landslide inventory from 2004 to 2018. Specifically, this framework assumes that the occurrence and size of landslides spatiotemporally follows a binomial and a Log-Gaussian distribution, respectively, and then uses generalized additive models to achieve the estimation of landslide hazard probability. Finally, the performance is validated by a spatiotemporal leave-one-out cross-validation scheme. We believe that this framework will lay the foundation for the community to estimate landslide hazard in a unified and probabilistic data-driven prototype. We envision it could lead to studies of dynamic hazard responses to climate change.</p

Space–Time Landslide Susceptibility Modeling Based on Data-Driven Methods

Delineating spatiotemporal variations in landslide susceptibility patterns is crucial for landslide prevention and management. In this study, we present a space–time modeling approach to predict the annual landslide susceptibility of the main island of Taiwan from 2004 to 2018. Specifically, we use a Bayesian version of the binomial generalized additive model, assuming that landslide occurrence follows a Bernoulli distribution. We generate 46,074 slope units to partition the island of Taiwan and divide the time domain into 14 annual units. The binary states of landslide presence and absence are classified by a set of static and dynamic covariates. Our modeling strategy features an initial explanatory model to test for goodness of fit and to interpret the effects of covariates. Then, five cross-validation schemes are tested to provide the full range of the predictive capacity of our model. We summarize the performance of each test through receiver operating characteristic curves and their numerical variation over space and time. Overall, our space–time model achieves satisfactory results, with the mean AUC above 0.8. We believe this type of dynamic prediction is a new direction that eventually moves away from the static view provided by traditional susceptibility models. Meanwhile, we believe that such analyses are only stepping stones for further improvements, the most natural of which are statistical simulations of future scenarios.</p

Rapid Inventory of Earthquake Damage (RIED)

The 25 January 1999 Quindío earthquake in Colombia was a major disaster for the coffee-growing region in Colombia. Most of the damage occurred in the city of Armenia and surrounding villages. Damage due to earthquakes is strongly related to topographic and subsurface geotechnical conditions underneath structures and houses. The RIED project used aerial photographs to obtain a rapid inventory of the earthquake damage right after the seismic event. This inventory was subsequently used to identify any existing relation with subsurface- and topographic conditions. Hazard zonation maps were made on the basis of seismic response analysis of a three-dimensional model of the subsurface that has been created in the GIS. Also indicative zonation maps were created outlining potential areas where topographic amplification may occur. These seismic zonation maps delineate those areas that are most likely affected by subsurface and topographic resonance effects during a future and similar earthquake. The maps have been presented to the city planning authorities of Armenia so that reconstruction of the damaged areas can be carried out in such a way that high risk areas will be avoided or that structures and houses will be built according to the standards for high seismic risk areas

Application of beta regression for the prediction of landslide areal density in South Tyrol, Italy

The concept of landslide hazard entails evaluating landslide occurrence in space (i.e., where landslides may occur), in time (i.e., when or how often landslides may occur), and their intensity (i.e., how destructive landslides may be). At regional scales, data-driven methods are implemented to separately analyze the spatial component (i.e., landslide susceptibility) and the temporal conditions leading to landslide occurrence, such as rainfall thresholds. However, assessing how large a landslide may develop once triggered is seldom conducted and poses a persistent challenge to satisfying the complete definition of landslide hazard.So far, only a few publications have addressed this issue by predicting the total areal extent of landslides based on certain mapping units, such as slope units. Limitations arise since the total areal extent of landslides within a mapping unit is strongly influenced by the size of the mapping unit, leading to larger mapping units being more likely to encompass larger total landslide areas. To tackle these challenges, this study aims to predict the landslide area proportion per slope unit in South Tyrol, Italy (7,400 km²). Our approach built upon past landslide occurrences from 2000 to 2020, systematically related to damage-causing and infrastructure-threatening landslide events. The method involved delineating slope units, filtering the landslide inventory, designing the sampling strategy, removing trivial areas, and aggregating the environmental variables (e.g., topography, lithology, land cover, and precipitation) to the slope unit partition. We tested a generalized additive beta regression model to estimate statistical relationships between the various static predictors and the target landslide areal density. The resulting spatially explicit predictions are evaluated through cross-validation from multiple perspectives. Applications and shortcomings of the approach are discussed.The proposed method is anticipated to provide valuable insights and alternatives to assessing landslide intensity and moving toward landslide hazard in a data-driven context. The outcomes associated with this research are framed within the PROSLIDE project, which has received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige

Analisis dan Estimasi Dampak Longsorlahan terhadap Jaringan Jalan di Kecamatan Samigaluh, Kabupaten Kulonprogo

In this study, direct risk assessment was developed for various scenarios on the basis of hazard (e.g. spatial probability, temporal probability and magnitude class), vulnerability and estimating cost of road damage. Indirect risk assessment was derived from traffic interruption. The impact of landslide both direct and indirect impact were analyzed in the road segment 174. The research results show the highest direct impact of debris slide type of magnitude I located in the 20th mapping unit. The lowest direct impact of debris slide type of magnitude I can be founded in the 18th mapping unit. The direct impact of rock fall type of magnitude I which is located in the 6th mapping unit. Meanwhile, indirect impact which was caused by road blockage is Rp. 4,593,607.20 and Rp. 4,692,794.40 by using network analysis and community perception methods respectively. After class classification, road segment 174 is dominated by very low hazard, very low vulnerability and very low direct impact

Analysing the outbreaks of leptospirosis after floods in Kerala, India

A growing number of studies have linked the incidence of leptospirosis with the occurrence of flood events. Nevertheless, the interaction between flood and leptospirosis has not been extensively studied to understand the influence of flood attributes in inducing new cases. This study reviews leptospirosis cases in relation to multiple flood occurrences in Kerala, India. Leptospirosis data were obtained for three years: 2017 (non-flood year) and two years with flooding—2018 (heavy flooding) and 2019 (moderate flooding). We considered the severity of flood events using the discharge, duration and extent of each flooding event and compared them with the leptospirosis cases. The distribution of cases regarding flood discharge and duration was assessed through descriptive and spatiotemporal analyses, respectively. Furthermore, cluster analyses and spatial regression were completed to ascertain the relationship between flood extent and the postflood cases. This study found that postflood cases of leptospirosis can be associated with flood events in space and time. The total cases in both 2018 and 2019 increased in the post-flood phase, with the increase in 2018 being more evident. Unlike the 2019 flood, the flood of 2018 is a significant spatial indicator for postflood cases. Our study shows that flooding leads to an increase in leptospirosis cases, and there is stronger evidence for increased leptospirosis cases after a heavy flood event than after a moderate flooding event. Flood duration may be the most important factor in determining the increase in leptospirosis infections.</p

Designing a spatial planning support system for rapid building damage survey after an earthquake: The case of Bogota D.C., Colombia

Damage assessment determines the safe condition of houses and buildings that were affected by a disaster. These elements must be inspected to determine if they can be occupied by people. The objective of the present research is to design a model for the planning of a rapid building damage survey after an earthquake and manage the spatial information collected. The model is built on three sub-models aiming to estimate the number of trained people required, their spatial allocation and the right information flow. The combination of cadastral data and organizational issues will be the input, to estimate the number of trained people required. To allocate the trained people, five methods were applied: average number of parcels or blocks, euclidean allocation, multiple-ring buffer, network analysis (service area), and route allocation. All the data required to respond in an emergency must be collected, updated and shared in order to have informed decisions. The results show wide ranges of values that can be utilized in the preparedness or in the response phase; the allocation methods can be used according to the data that every city has, but the highest level of accuracy comes from the route allocation method. The data must be available, updated and accessible to all the entities involved in the emergency response task, due to these reasons the research recommends the implementation of a Spatial Data Infrastructure (SDI) to manage the information and to predefine the meeting points to compile the collected information by using methods as mean center

From spatio-temporal landslide susceptibility to landslide risk forecast

The literature on landslide susceptibility is rich with examples that span a wide range of topics. However, the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored. This statement holds true, particularly in the context of landslide risk, where few scientific contributions investigate risk dynamics in space and time. This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years (from 2013 to 2021). For the analyses, the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit, resulting in a total of 236,997 units. This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature (e.g., variable interaction plots). However, the main innovative effort is in the subsequent phase of the protocol we propose, as we used climate projections of the main trigger (rainfall) to obtain future estimates of yearly susceptibility patterns. These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model, assuming vulnerability = 1. Overall, this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.</p