Hazard Mitigation Strategies Research Articles

Bayesian networks and intelligence technology applied to climate change: An application of fuzzy logic based simulation in avalanche simulation risk assessment using GIS in a Western Himalayan region

Avalanches are snowfalls that cascade down a mountainside, although rockfalls and debris flows can also occur. The study identifies the snow meteorological data for the avalanche risk forecast through various associated factors and models a tool for forecasting avalanches according to the reference database. The terrain factors such as aspect, slope, and altitude have been studied using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) at 90 m resolution. Snow meteorological observed data 2540 records were obtained for Snow and Avalanche Study Establishment (SASE) Nov 2004 to April 2009 for five years data. In the present study, nine parameters were used for Avalanche forecast in the Western Himalaya. The Bayesian classification algorithm may improve the feedback system of prediction than the existing one. Fuzzy logic decides to improve the system to predict the level of danger by studying the influence of parameters. Hazard mitigation strategies applying the Bayesian classification algorithm and Fuzzy logic were combined with Integrated GIS-based methods to determine the risk zones better to identify snow avalanches in three-dimensional terrain.

Multidimensional soil salinity data mining and evaluation from different satellites

Soil salinization, a common land degradation mode, restricts the ecological environment and is a global issue due to climate change. Accurately, quickly and effectively monitoring soil salinity is critical for governmental institutions that develop hazard prevention and mitigation strategies. Remote sensing (RS) technology provides a viable alternative to traditional field work due to its large area coverage, abundant spectral information and nearly constant observations. Key issues in RS-based soil salinity monitoring include the lack of both data-mining techniques for obtaining spectral band information and comprehensive considerations of synergies among different spectra. The main objective of this study was to provide in-depth explorations of data mining and integration algorithms from different satellites to multidimensionally evaluate soil salinity models. The Ebinur Lake Wetland Reserve (Xinjiang Province, China) was selected as a case study. First, ground-measured visible and near infrared (VIS-NIR) spectral data were combined with the RS band to simulate Landsat 8 (L8) and Sentinel 2 (S2) and 3 (S3) data. Second, one-dimensional RS bands and 15 soil salinity and vegetation indices were selected, and 15 spectral data transformations (reciprocal, differential, absorbance, etc.) were obtained. Two- and three-dimensional spectral indices were constructed, and the response relationships between different spectral indices and soil electrical conductivity (EC) were comprehensively explored. Finally, an integrated multidimensional algorithm was used to estimate soil salinity in high-performance models for the three satellites. The results showed that all data-mining-based model combinations performed well for all satellites (R2 > 0.80). However, with multidimensional model combinations, S3 presented the highest predictive capability (R2 = 0.89, RMSE = 2.57 mS·cm−1, RPD = 2.05), followed by S2 (R2 = 0.86, RMSE = 2.71 mS·cm−1, RPD = 1.90) and L8 (R2 = 0.85, RMSE = 2.84 mS·cm−1, RPD = 1.87). Therefore, data mining with integration algorithms in model combinations performs significantly better than previous models and could be considered a promising method for obtaining improved results from soil salinity susceptibility models in similar cases.

Landslide Susceptibility Evaluation Using Different Slope Units Based on BP Neural Network.

Landslides are one of the most widespread natural hazards that cause damage to both property and life every year. Therefore, the landslide susceptibility evaluation is necessary for land hazard assessment and mitigation of landslide-related losses. Selecting an appropriate mapping unit is an essential step for landslide susceptibility evaluation. This study tested the back propagation (BP) neural network technique to develop a landslide susceptibility map in Qingchuan County, Sichuan Province, China. It compared the results of applying six different slope unit scales for landslide susceptibility maps obtained using hydrological analysis. We prepared a dataset comprising 973 historical landslide locations and six conditioning factors (elevation, slope degree, aspect, lithology, distance to fault lines, and distance to drainage network) to construct a geospatial database and divided the data into the training and testing datasets. We based on the BP learning algorithm to generate landslide susceptibility maps using the training dataset. We divided Qingchuan County into six different scales of slope unit: 4,401, 13,146, 39,251, 46,504, 56,570, and 69,013, then calculated the receiver operating characteristic (ROC) curve, and used the area under the curve (AUC) for the quantitative evaluation of 6 different slope unit scales of landslide susceptibility maps using the testing dataset. The verification results indicated that the evaluation generated by 56,570 slope units had the highest accuracy with a ROC curve of 0.9424. Overelaborate and rough division of slope units may not get the best evaluation results, and it is necessary to obtain the slope units most consistent with the actual situation through debugging. The results of this study will be useful for the development of landslide hazard mitigation strategies.

Impact of variation of size of the initial release mass in the dynamics of landslide generated tsunami

When debris flows, landslides, or any gravitational mass flows hit closed or partially open water sources such as seas, oceans, fjords, hydraulic reservoirs, mountain lakes, bays, and landslide dams, it results in tsunami (impulse water waves) by transforming their impact energy to water body, potentially causing damages of infrastructures and human casualties both near field and the distant coastlines. The intensity of hazard depends on the scale, location and process of the landslide, and also on the reservoir volume and topography that surrounds it. Volume or size of the initial release mass that fails and slides along a slope is one of the dominant factors to determine the degree of splash, propagating speed and the amplitudes of the fluid waves, and potential dam breach or water spill over. Here, we numerically integrate the two-phase mass flow model [Pudasaini S. P., J. Geophysi. Res. 117 (F03010), 2012] for quasi-three-dimensional, high-resolution simulation results with variation of size of the two-phase initial landslide or debris both longitudinally and laterally. In our numerical experimental results, we observe fundamentally different solid and fluid evolution and wave structures in the reservoir. There are also significant differences in the flow dynamics of solid under water for different volumes of the release mass by extending or contracting the base area along downslope and/or cross-slope directions. The simulation results show that tsunami amplitudes and run out extents are rapidly increased when the volume of the initial release mass in the form of a triangular wedge is increased by increasing the base area through the increment of the length and breadth of the release base. This study can be useful to develop and implement tsunami hazard mitigation strategies to enhance public safety and reduce potential loss due to landslide-generated wave hazards.

Modelling the Effect of ‘Roller Dynamics’ on Storm Erosion: Sylt, North Sea

Coastal storm erosion can lead to episodic morphological changes and hinterland flooding that requires sustainable management. An accurate estimation of storm erosion can determine the success of hazard mitigation strategies. Two morphological models, Delft3D and XBeach, were applied separately to a stormy period with “Roller” and “No Roller” wave dynamics activated, to estimate erosion of the beach and dune system on the Sylt island. This is the first numerical impact assessment of roller dynamics on coastal erosion using the two models. The choice of model had more impact on the hydrodynamic and morphological predictions than the option to include or omit roller dynamics. Agreement between measured and simulated waves was higher in Delft3D (R2 > 0.90 and RMSE < 0.15 m) than XBeach. Storm erosion in both models had the highest sensitivity to the roller parameter Beta. Both models predicted a similar storm erosion pattern along the coast, albeit different magnitudes. It is found that Delft3D cannot produce comparable storm erosion to XBeach, when the roller dynamics and avalanching are considered. Delft3D is less sensitive to the roller dynamics than XBeach. Including roller dynamics in Delft3D increased storm erosion up to 31% and in XBeach decreased the erosion down to 58% in the nearshore area, while the erosion in the dune area increased up to 13% in Deflt3D and up to 97% in XBeach. Both models are skilled in simulating storm impact. For the simulation of a storm period with intermittent calm periods, it is suggested that applying a time-varying parameter setting for wave dynamics and sediment transport to capture storm erosion and post-storm beach recovery processes could improve results. Such a modelling approach may ultimately increase the accuracy of estimating storm erosion to support coastal management activities (e.g., sand nourishment volume).

Multi‐agent modeling of hazard–household–infrastructure nexus for equitable resilience assessment

AbstractInfrastructure service disruptions impact households in an affected community disproportionally. To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi‐agent simulation model, which enables integrated assessment of hazard, infrastructure system, and household elements and their interactions. With a focus on hurricane‐induced power outages, the model consists of three elements: (1) the hazard component simulates exposure of the community to a hurricane with varying intensity levels; (2) the physical infrastructure component simulates the power network and its probabilistic failures and restoration under different hazard scenarios; and (3) the households component captures the dynamic processes related to preparation, information‐seeking, and response actions of households facing hurricane‐induced power outages. We used empirical data from household surveys from three hurricanes (Harvey, Florence, and Michael) in conjunction with theoretical decision‐making models to abstract and simulate the underlying mechanisms affecting the experienced hardship of households when facing power outages. The multi‐agent simulation model was then tested in the context of Harris County, Texas, and verified and validated using empirical results from Hurricane Harvey in 2017. Then, the model was used to examine the effects of different factors—such as forewarning durations, social network types, and restoration and resource allocation strategies—on reducing the societal impacts of service disruptions in an equitable manner. The results show that improving the restoration prioritization strategy to focus on vulnerable populations is an effective approach, especially during high‐intensity events, to enhance equitable resilience. The results show the capability of the proposed computational model for capturing the dynamic and complex interactions in the nexus of households, hazards, and infrastructure systems to better integrate human‐centric aspects in resilience planning and assessment of infrastructure systems in disasters. Hence, the proposed model and its results could provide a new tool for infrastructure managers and operators, as well as for disaster managers, in devising hazard mitigation and response strategies to reduce the societal impacts of power outages in an equitable manner.

Estimating the velocity of pyroclastic density currents using an operational dual-PRF radar

Pyroclastic density currents are one of the deadliest hazards produced by a volcano. Understanding their dynamics and generation mechanisms is critical for developing better hazard mitigation strategies. This study presents a method for retrieving velocity profiles across a natural moving PDC, applied here to a PDC generated by collapsing column during the eruption of Sinabung Volcano, Indonesia, on 19 February 2018 at the onset time of 08:53. We used an operational dual Pulse Repetition Frequency (PRF) weather radar, located ~7.8 km to the SE of the volcano, to estimate the velocity profile components of the volcanic plume: updraft, fallout, and horizontal advection. Doppler radar data was post-processed by applying two different filters: median and Laplacian, to correct errors associated with dealiased Doppler velocities. The Laplacian filter method was more effective in correcting the dealiasing errors by producing a more continuous velocity field without over smoothing its values. Following the dealiasing process, the velocity profile components were retrieved according to radar parameters such as Doppler velocities, copolar correlation, and reflectivity intensity factor. Initially, the pyroclastic clast was released at a lower exit velocity of ~120 m/s (84 s after the onset). A maximum of ~190 m/s exit velocity was then observed at 08:57:52 (292 s after onset). Lower exit velocity in the initial phase and less than 10 m/s estimated wind speed are the main factors causing the partial collapse of the plume at ~2.5 km height above the vent. The part of the collapsing column was associated with a more than 50 dBZ reflectivity intensity factor of fallout velocity exceeding −50 m/s at 126 s after the onset. Dilute PDCs were observed until 09:09:34 (994 s after onset), moving downslope at SE sector at a maximum velocity of −84 m/s (i.e., in the direction of the radar) . The extracted velocity components are essential parameters in the numerical model of PDCs and tephra dispersal, enforcing the benefit of weather radar to complement the remote monitoring system of volcanic hazards.

Dissecting lightning strike hazard impact patterns to National Airspace System facilities in the contiguous United States

Lightning strikes pose a severe threat to the United States (US) National Airspace System (NAS). Although the US Federal Aviation Administration (FAA) implements lightning protection practices and procedures to protect personnel, electronic equipment, and structures within the NAS, many lightning-induced outages still occur. To date we found that most research on lightning-induced facility outages has focused on understanding the physical processes of lightning strike effects on aircraft and airport ramp operations. Very little research has been done on examining the overall patterns and characteristics of such hazards to aviation from a geo-spatial standpoint. To bridge this gap, we analyze nationwide lightning strike spatiotemporal data and FAA airport facility outage records from 2009 through 2020 and apply innovative pattern recognition methods to identify key characteristics of lightning strike hazards. Our results uncover the complexities of lightning strike hazard impact patterns to NAS facilities, identifying five distinct typologies with climatological signatures critical to creating better hazard mitigation strategies.

Understanding asteroidal failure through quasi‐static compression testing and 3‐D digital image correlation of the Aba Panu (L3) chondrite

AbstractA comprehensive understanding of the mechanical strength and failure mechanisms of asteroids is essential for the development of hazard mitigation strategies and in situ resource extraction. In this study, the Aba Panu (L3) ordinary chondrite meteorite is investigated to understand its failure response under compressive loading. Compression experiments were conducted on ten 1 cm cubes under quasi‐static conditions in constant displacement control mode at room temperature. Three‐dimensional (3‐D) digital image correlation (DIC) was used to measure the full‐field deformation and strain. These data were used to determine the elastic modulus and local strain distribution, and investigate the effects of the mineralogical and structural heterogeneity on the crack formation and growth sites. Aba Panu exhibits brittle failure during compression with a range of failure strength from 361.7 to 578.0 MPa. Axial splitting and multiple fracturing occur during the uniaxial compressive state of stress. Ultrasonic tests were used to calculate elastic moduli from sound speeds and compared with the results of L‐type ordinary chondrites from the literature. Characterization results from electron microprobe analysis identified different elements and areal distribution of mineral phases and pre‐existing cracks. In general, the DIC results did not show correlations between crack initiation and specific mineralogical or textural components in this meteorite, such as chondrules or metals/sulfide grains, suggesting that the pre‐existing microcracks and porosity control the Aba Panu failure mechanisms.

Investigation of Geological Structures Using UAV Lidar and Its Effects on the Failure Mechanism of Deep-Seated Landslide in Lantai Area, Taiwan

The deep-seated landslide in the Lantai area, Taiwan, has a long history of landslide activity and often damages the sole access road to the Tai-Ping Mountain National Forest Recreation Area. This study adopted the high-resolution digital terrain model (DTMH) derived from UAV mounted LiDAR point cloud data for mapping geological structures and verified through field investigation. A slope model was proposed with mapped geological structures and shear zone, and numerical analysis was conducted using finite difference analysis. The failure mechanism was found to be significantly affected by the shear zone bounded by geological structures, which would not have been uncovered without the high-resolution DTM (DTMH). The resulting landslide behavior consisted well with mapped scarp, borehole data, and conformed with the event records. These results provided vital information supporting hazard mitigation strategy.

Surface trace of the active Katrol Hill Fault and estimation of paleo-earthquake magnitude for seismic hazard, Western India

Estimation of seismogenic potential through geological studies of faults is an important step in seismic hazard evaluation and mitigation. We investigated the Katrol Hill Fault (KHF), an E-W trending and south dipping high angle reverse fault in the seismically active Kachchh Basin at the western continental margin of India. While several faults in the eastern part of the basin are presently seismically active, the KHF is the only fault in the entire basin that is shown to have produced three Late Quaternary surface faulting events. We illustrate here an integrated geological approach using field mapping, microscopic analysis (petrography and quartz surface textures using SEM) and shallow geophysical surveys using GPR for determining the precise surface trace of the active KHF. We also determined the displacement and slip-rate for each surface faulting event. Based on the Late Quaternary surface faulting parameters deduced, we calculated the moment magnitude (Mw) of the events using well established empirical relationships. These calculations yielded Mw values consistently ≥ 6.5. Our study shows that the KHF is capable of producing high magnitude earthquakes. We infer that these surface faulting events may be of relatively shallow focus as higher magnitude earthquakes in recent times along other faults in the basin, except the 1819 Allahbund earthquake, did not produce surface rupture. We recommend a revision of seismic hazard risk and mitigation strategies of the region as a consequence of our findings.

A brief review of the recent Assam Earthquake

The recent Assam Earthquake has been previewed in the context of the recent seismicity pertaining to Kopili Fault of northeast India. Kopili fault of northeast India is known for its active seismicity. The ground motion parameters in the context of peak ground acceleration, peak ground velocity and peak spectral acceleration stand out for their implications. Considering the strong magnitude of the event, there is imminent need for earthquake resilient buildings as well as hazard mitigation strategies in order to tackle post-earthquake scenarios.

Rain, Snow and Frozen Soil: Open Questions from a Porescale Perspective with Implications for Geohazards

Climate change is already affecting high mountain regions, such as the European Alps. Those regions will be confronted with a significant rise of temperatures above the global average, and more and heavier rain events, also during wintertime. The system response to the coincidence of rain, snow, and possibly frozen soil depends on the almost infinite number of possible combinations of thermo-hydraulic states of the involved phases. Landslides, snow avalanches, debris flows, or extensive surface runoff are just a few of the possible hazardous outcomes. With rising temperatures and increased precipitation, those hazardous outcomes are expected to occur even more frequently in the future, requiring a better understanding of those coupled processes for hazard mitigation strategies. The macroscopic phenomena are controlled by porescale processes, such as water freezing and ice grains blocking pores, which are only barely understood. The strong coupling between thermal state and hydraulic parameters, the possible phase change, and material heterogeneity pose great challenges for investigation. This work provides an overview of documented hazard events regarding rain, snow, and possibly frozen soil. The current state in theoretical and experimental research is presented before several knowledge gaps are derived and possible techniques to address those gaps are discussed.

Rockfall Hazard Analysis Based on the Concept of Functional Safety with Application to the Highway Network in South Korea

Numerous rockfall incidents involving infrastructure damage and loss of life have been reported along roads in mountainous terrain. Previous studies have used quantitative risk assessment approaches to identify the level of rockfall risk. However, appropriate quantitative indicators that are able to describe time-varying risk have not yet been developed. This study aims to develop a rockfall risk mitigation method based on reliability concepts, to classify rockfall data, to model the probability of rockfall occurrence, and to estimate the magnitude of risk reduction through mitigation measures. A synthetic measure of rockfall risk is proposed, which allows to compare directly and quantitatively the rockfall risk for different cut slopes under unmitigated and mitigated conditions. The proposed methodology can estimate the risk reduction obtained using mitigation measures, such as introducing protections barriers, their periodic maintenance, and horizontal coverage ratio. This methodology was applied to 20 years of rockfall data collected by the Korea Expressway Corporation from 1215 artificial cut slopes along the highway network in South Korea. The rockfall frequency was analyzed based on the inventory data, and a rockfall hazard mitigation strategy was demonstrated using the suggested methodology for the case study. It was shown that appropriate mitigation strategies, based on number of protection barriers, interval of periodic maintenance, and horizontal coverage ratio, can be devised to reduce the risk of different artificial slopes below a target failure probability. The approach shown in this study can provide insights into ways of improving overall risk management to prevent losses by rockfall.

Application of Spatial Multi-Criteria Analysis (SMCA) to assess rockfall hazard and plan mitigation strategies along long infrastructures

Long infrastructures often cross areas with a high probability of landslides, causing eventually serious problems to the serviceability and compromising safety. The identification and prediction of hazardous zones are difficult, especially for what concerning rockfalls, as they can occur quickly and suddenly. In order to assess rockfall hazard, detailed data such as slope geometry, geotechnical and geomechanical properties of materials, drainage system pattern, etc. are needed. Even though thematic datasets are available and easily downloadable for the majority of the Italian territory, their scale is not adequate and ad-hoc input data must be gathered. An original multi-disciplinary procedure (GEO4) has been developed by the authors based on a mobile mapping system (ARCHITA) integrated with Airborne Lidar and ILI (In-Line Inspections), geomatics, geological models, geotechnical-geomechanical characterization and geomorphometric approach. A Spatial Multi-Criteria Analysis (SMCA) is then used to create a composed and spatially distributed index of landslide hazard based on normalized values of triggering factors. Such index is used to identify and classify the morphological unstable element along the infrastructure, supporting decision-makers in defining the most appropriate mitigation measures and planning their implementation in a clearer, more repeatable and more objective orientated-way. The presented method has been successfully applied so far to hundreds of km of railway lines in Italy.

Spatial-scale dependent risk factors of heat-related mortality: A multiscale geographically weighted regression analysis

Extreme heat is a leading cause of weather-related human mortality throughout much of the world, posing a significantly heavy burden on the development of healthy and sustainable cities. To effectively reduce heat health risk, a better understanding of where and what risk factors should be targeted for intervention is necessary. However, little research has examined how different risk factors for heat-related mortality operate at varying spatial scales. Here, we present a novel application of the multiscale geographically weighted regression (GWR) approach to explore the scale of effect of each underlying risk factor using Hong Kong as a case study. We find that a hybrid of global and local processes via multiscale GWR yields a better fit of heat-related mortality risk than models using GWR and ordinary least squares (OLS) approaches. Predictor variables are categorized by the scale of effect into global variables (i.e., age and education attainment, socioeconomic status), intermediate variables (i.e., work place, birth place and language), and local variables (i.e., thermal environment, low income). These findings enrich our understanding of the spatial scale-dependent risk factors for heat-related mortality and shed light on the importance of hierarchical policy-making and site-specific planning processes in effective heat hazard mitigation and climate adaptation strategies.

GIS-Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China

Landslides are one of the nature hazards causing a lot of casualties and property losses in the world. Over the last decades, many researchers have made contributions in landslide susceptibility maps using qualitative and quantitative methods. Parameters of DEM, geology, etc. are selected to analyze the mechanism of landslides. The quality of data is essential in the landslide studies, and more credible results can be obtained if the data is adequate and accurate from the wide range of parameters. The aim of this study is to evaluate the landslide susceptibility of Huangyuan County of Qinghai. Through field investigations, 100 landslide disaster locations in the study area were selected, and 11 influencing factors including elevation, slope, aspect, plane curvature, profile curvature, road distance, river distance, fault distance, stratum rock property, vegetation coverage index, and terrain humidity index were selected as the influencing factors of landslide disaster based on GIS. In this paper, the information method (IM) model, frequency ratio (FR) model, and artificial neural network (ANN) model are used to evaluate the susceptibility of geological hazards, and the receiver operating characteristic (ROC) curve of disaster points at different levels is used to test the evaluation accuracy of three models. The results show that factors that have great influence on landslides are associated with witness, and the terrain humidity index has the highest weight in the occurrences of landslide. The values of AUC indicate that the ANN model is the best evaluation model suitable for the study area and can be extremely useful for landslide hazard mitigation strategies. Based on the calculation of ANN model, three valley areas are determined with high landslide susceptibility, and necessary reinforcement measures should be taken.

Development of smart boulders to monitor mass movements via the Internet of Things: a pilot study in Nepal

Abstract. Boulder movement can be observed not only in rockfall activity, but also in association with other landslide types such as rockslides, soil slides in colluvium originating from previous rockslides, and debris flows. Large boulders pose a direct threat to life and key infrastructure in terms of amplifying landslide and flood hazards as they move from the slopes to the river network. Despite the hazard they pose, boulders have not been directly targeted as a mean to detect landslide movement or used in dedicated early warning systems. We use an innovative monitoring system to observe boulder movement occurring in different geomorphological settings before reaching the river system. Our study focuses on an area in the upper Bhote Koshi catchment northeast of Kathmandu, where the Araniko highway is subjected to periodic landsliding and floods during the monsoons and was heavily affected by coseismic landslides during the 2015 Gorkha earthquake. In the area, damage by boulders to properties, roads, and other key infrastructure, such as hydropower plants, is observed every year. We embedded trackers in 23 boulders spread between a landslide body and two debris flow channels before the monsoon season of 2019. The trackers, equipped with accelerometers, can detect small angular changes in the orientation of boulders and large forces acting on them. The data can be transmitted in real time via a long-range wide-area network (LoRaWAN®) gateway to a server. Nine of the tagged boulders registered patterns in the accelerometer data compatible with downslope movements. Of these, six lying within the landslide body show small angular changes, indicating a reactivation during the rainfall period and a movement of the landslide mass. Three boulders located in a debris flow channel show sharp changes in orientation, likely corresponding to larger free movements and sudden rotations. This study highlights the fact that this innovative, cost-effective technology can be used to monitor boulders in hazard-prone sites by identifying the onset of potentially hazardous movement in real time and may thus establish the basis for early warning systems, particularly in developing countries where expensive hazard mitigation strategies may be unfeasible.

A s tudy on new amphibious housing solution for vulnerable communities in Kuttanad

Face of Kerala is changing with climate making it more vulnerable to flood and flood related hazards. Flood hazard mitigation strategies requires a greater degree of adaptability, which calls for the development of new housing types that build resilience among vulnerable population. Kuttanad is a low-lying deltaic region in Kerala with highest number of flood displaced population per year. The economically weaker section of this region is striving for a flood resilient house. Huge house damage loss followed by Kerala flood 2018 paved the way to think about a construction that lives with water without making any obstruction to its flow. Amphibious housing is a sustainable flood mitigation strategy that works in synchrony with the flood vulnerable region’s natural cycle of flooding. These type of constructions are already a part of Netherlands and Rural Louisiana since 1970’s. This paper reviews the case studies of existing amphibious buildings globally and present flood resilient constructions of Kerala. It also analyses the possibility and proposed a new amphibious housing solution based on timber for the vulnerable regions in Kuttanad. The material selection was based on certain parameters which ensure the sustainability of prototype. These type of construction can act as a permanent housing solution for the flood displaced population of Kuttanad.

Landslide susceptibility zonation in the Tartagal River basin, Sierras Subandinas, Salta, Argentina

On February 2009 intense rainfall triggered landslides in the Tartagal River basin that evolved into a debris flow that caused severe flooding in the town of Tartagal, Salta, Argentina. Based on these events, this paper presents a first attempt to map the landslides susceptibility in the Tartagal River basin. First, we elaborated an inventory map by using a 10 m pixel SPOT image acquired just after the disaster. Second, we evaluated a set of conditioning factors, which included lithology, slope and curvature; we derived the topographical variables from a 12.5 m pixel digital elevation model (DEM) based on a stereo-pair of satellite images ALOS-PRISM. Finally, we used these conditioning factors and the 2009 landslides inventory map as input for a heuristic model to elaborate the susceptibility map. The results indicated that landslides affected an area of 8 km2 and that at least 2.2x106 m3 of material were removed. The susceptibility map identified zones of low, moderate, high and very high susceptibility that occupied 18, 22, 25 and 17 km2, respectively. Accuracy assessment using data covering landslides occurred in 2006 showed that 95% of them fell within the high and very high susceptibility areas. The results presented herein provide vital baseline information for future studies and may contribute for the development of landslide hazard mitigation strategies.