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In June 2018, the Peruvian provinces of Arequipa and Puno in the southern Andean region were affected by heavy snowfall, which caused severe damage to people and livelihoods in several communities. Using the Forecast-based Financing approach, the Peruvian Red Cross implemented its pre-defined early action protocol before this event, after receiving an extreme snowfall warning (Level 4) from the Peruvian meteorological service. Here, we provide a case study of the approach and event itself, documenting the decision-making thresholds as well as the actions taken. This warning activated the thresholds established in the protocol, and Peruvian Red Cross prioritized 10 communities for pre-disaster support based on the forecasted severity of the event in combination with vulnerability and exposure information. The activation took place 2 days before the extreme snowfall in the communities, and the Red Cross distributed veterinary kits for 50 heads of cattle per family, tarpaulins, tool kits to install a temporary awning for alpacas to protect them from the cold wind and snow, protection kits for alpaca herders and warm clothing for children under five, pregnant women, the elderly, and people with chronic and infectious diseases in 430 highly vulnerable households. This article presents the results of a household survey following the impact of the extreme snowfall. We document the early actions taken by these communities to protect their livelihoods, health, and assets. The evaluation also presents descriptive statistics of household-level outcomes for households receiving pre-snowfall support and those that did not receive any intervention or only received post-disaster assistance. While most households took action to protect their assets, there were fewer extreme losses of alpaca herds reported in the communities who received the early support, and these communities also reported fewer adults suffering from respiratory illnesses. More in-depth research on this type of early action is necessary on a wider scale, especially to evaluate the utility of different support measures and the necessary quantity of support needed. This case study can inform government, civil society, and humanitarian actors of how early action is happening before disasters occur and provide a direction for further investment in research and practice to make use of hydro-meteorological forecasts for the benefit of the most vulnerable. Copyright © 2021 Bazo, de Perez, Jacome, Mantilla, Destrooper and Van Aalst.

The Himalayan regions are vulnerable to all kinds of natural hazards. On 7 February 2021, a deadly disaster occurred near the Tapovan, in Uttarakhand, Himalayas. During the event, large volume of debris along with broken glacial fragments flooded the Rishi Ganga River and washed away the nearby hydropower plants (Rishi Ganga and Tapovan), which was revealed from detailed analysis of multi spectral and bi-temporal satellite data. We present the impact of the Chamoli disaster on the flood plains and water quality of Himalayan rivers, Rishi Ganga near Tapovan, Alaknanda near Srinagar and Ganga near Haridwar and Bijnor. We used four locations along four sections of Himalayan rivers and have analysed various indices, modified normalized difference water index, normalized difference chlorophyll index, and normalized difference turbidity index, to study the changes in water quality and flood plains. On comparison of the spectral and backscattering coefficients derived from Sentinel-2 optical and Sentinel-1 synthetic aperture radar data, changes in the water quality and flood plains of the rivers were found.

Sahelian West Africa is a region of high year-to-year climate variability that can significantly impact on communities and livelihoods. Adaptive social protection (ASP) is being introduced in the region to support vulnerable people through enabling more effective responses to climate shocks, bridging social protection with disaster risk reduction and climate change adaptation. The ASPIRE (Adaptive Social Protection: Information for enhanced REsilience) project aimed to provide technical support to the World Bank’s Adaptive Social Protection Programme through demonstrating the use of climate forecasts in ASP and promoting dialogue between climate and social protection stakeholders. Here we discuss lessons learned in the project, highlighting challenges and opportunities for including climate forecasts in early warning systems to inform ASP. We provide recommendations to help achieve ASP systems designed to use climate forecasts, arguing that tailored seasonal forecast products have potential in some countries to improve the lead time of interventions to address climate-induced disasters. Critical to this is continued investment in underpinning science and capacity building of climate and social protection stakeholders, as well as continued dialogue and co-production between actors to develop climate forecasts that provide actionable information.

Hydro-morphological processes (HMPs) are some of the most destructive natural disasters. Understanding the spatiotemporal characteristics of HMPs across China is important for enabling better disaster estimation and prevention at the national scale. However, few studies have focused on the spatiotemporal HMP characterization under various geomorphic settings in China, which may shed light on their regional/national evolution. To bridge this research gap, we analysed the longest HMP time series available in China, which including 47,483 HMP records, to detect spatiotemporal patterns. Specifically, we run several tests namely, Mann–Kendall test, wavelet analysis, Monthly Frequency, and Index of Dispersion, with the objective of detecting the temporal evolution, trends, period, and clustering of HMPs in six geomorphological regions (geomorphic-regions): Eastern Plain, South Eastern Mountain (SEM), North Central Plateau, North Western Basin, South Western Mountain (SWM), and Tibetan Plateau. Our results show that in the last decades have been associated with a marked increase in HMPs and this should be accounted for, especially in those areas where we have retrieved high spatiotemporal clustering (e.g., SEM, SWM). Besides, the main periodicity of HMPs is approximately 12–25 years for most of China since the 1980s, which showed analogous patterns with precipitation anomalies. This study provides a preliminary reference for revealing the spatiotemporal characteristics of HMPs in the context of climate change; therefore, the information provided can be crucial to plan engineering applications with specific return period.

Rapid urbanization and increasing population in cities with a large portion of them settled in deprived neighborhoods, mostly defined as slum areas, have escalated inequality and vulnerability to natural disasters. As a result, monitoring such areas is essential to provide information and support decision-makers and urban planners, especially in case of disaster recovery. Here, we developed an approach to monitor the urban deprived areas over a four-year period after super Typhoon Haiyan, which struck Tacloban city, in the Philippines, in 2013, using high-resolution satellite images and machine learning methods. A Support Vector Machine classification method supported by a local binary patterns feature extraction model was initially performed to detect slum areas in the pre-disaster, just after/event, and post-disaster images. Afterward, a dense conditional random fields model was employed to produce the final slum areas maps. The developed method detected slum areas with accuracies over 83%. We produced the damage and recovery maps based on change analysis over the detected slum areas. The results revealed that most of the slum areas were reconstructed 4 years after Typhoon Haiyan, and thus, the city returned to the pre-existing vulnerability level.

Frequent landslide events affect the Kathmandu Kyirong Highway (KKH), one of the most strategic Sino-Nepal highways, with multiple social effects. Amongst them, the impacts on local tourism, although being substantial, have not been studied so far. The aim of this research is to analyze the characteristics of such landslides and their influence on road damages and/or blockages as well as on local tourism industry. We analyzed the co-seismic landslides triggered by the Gorkha Earthquake, 2015 (7.8 Mw), the post-seismic landslides that occurred during the monsoons following the earthquake, as well as landslides which occurred or reactivated in 2018, with relation to the damage that they caused to the highway. High resolution satellite images from 2015 to 2018, and field data were used for the analysis. The Langtang avalanche that locates off the highway was also mapped due to its high impacts on tourism. Between 2015 and 2018, the number of road damaging landslides in the Betrawati-Rasuwagadhi section of KKH (where Dhunche and Syafrubesi towns are located) was 101 in the main track (MT) and 103 in the new track (NT), with respective average density of 1.46/km and 3.63/km. The dominant observed landslide types were debris slides and rock falls. Landslides were mostly concentrated in the locations with the following characteristics: 1) having higher elevated area, 2) being located with the ‘main central thrust’ and other lineaments’ belts, 3) belonging to the Proterozoic lesser Himalayan rocks, 4) having a slope gradient of 25°–45°, 5) having northern, western and southern slope aspect, 6) being subjected to average annual rainfall of higher than 1,000 mm, and 7) having less than 4 km distance from the past earthquake epicenters. The results further indicated that 7 rain-induced and 4 co- and post-seismic landslides have great impact on tourist flows. An impact analysis was also assessed through a door to door questionnaire survey with local hotel operators from Dhunche and Syafrubesi towns (n = 29 + 31). The results reveal that out of six rigorously affected sectors by landslides leading to road blockage, tourism business is the most impacted livelihood sector in these towns. The reduction of visitors in different hotels ranged from 50%–100% in Dhunche and 70%–100% in Syafrubesi for the first year aftermath of the tremor. This is higher than the respective 5%–50% tourist reduction due to rain-induced landslides. Using as a reference the base year 2014, the income loss of hotels in both towns was found to be 50%–100% in 2015, 20%–100% in 2016, 5%–75% in 2017, and similar to 35% in 2018. These results provide insights on the synergic effect of contributing factors for cut slope as well as down slope instability along mountainous motorways and their impact on income sources for local communities.

Botswana experienced a Mw 6.5 earthquake on 3rd April 2017, the second largest earthquake event in Botswana's recorded history. This earthquake occurred within the Limpopo-Shashe Belt, ~350 km southeast of the seismically active Okavango Rift Zone. The region has no historical record of large magnitude earthquakes or active faults. The occurrence of this earthquake was unexpected and underscores our limited understanding of the crustal configuration of Botswana and highlight that neotectonic activity is not only confined to the Okavango Rift Zone. To address this knowledge gap, we applied a regularized inversion algorithm to the Bouguer gravity data to construct a high-resolution crustal thickness map of Botswana. The produced crustal thickness map shows a thinner crust (35–40 km) underlying the Okavango Rift Zone and sedimentary basins, whereas thicker crust (41–46 km) underlies the cratonic regions and orogenic belts. Our results also show localized zone of relatively thinner crust (~40 km), one of which is located along the edge of the Kaapvaal Craton within the MW 6.5 Botswana earthquake region. Based on our result, we propose a mechanism of the Botswana Earthquake that integrates crustal thickness information with elevated heat flow as the result of the thermal fluid from East African Rift System, and extensional forces predicted by the local stress regime. The epicentral region is therefore suggested to be a possible area of tectonic reactivation, which is caused by multiple factors that could lead to future intraplate earthquakes in this region.

There is considerable interest in optimizing geothermal exploration techniques via the mapping of alteration and evaporate mineralisation, as well as of thermal emissions associated with geothermally active areas on the Earth’s surface. Optical and thermal satellite sensor technologies, improvements in processing algorithms and the means for large scale (e.g. 1:250,000) spatial data distribution are required for detecting both these attributes. The extensive visible, -near, -shortwave and thermal infrared (VNIR-SWIR-TIR) data archive acquired by the multi-spectral Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER) provides a rich source of geoscience related imagery for geothermal exploration. Examples of generating large scale mosaicked ASTER imagery to provide province to continental mineral mapping have been undertaken in areas including such as Australia, western USA, Namibia and Zagros Mountains Iran. In addition, ASTER’s thermal infrared imagery also provides night time land surface temperature (LST) estimates relevant for detecting possible geothermal related anomalies. This study outlines existing methods for the application of ASTER data for geothermal exploration in East Africa. The study area encompasses a section of the East African Rift System across the Tanzanian and Kenyan border. The area includes rugged volcanic terrain which has had geological mapping of limited coverage at detailed scales, from various heritages and mapping agencies. This study summarizes the technology, the processing methodology and initial results in applying ASTER imagery for such compositional and thermal anomaly mapping related to geothermal activity. Fields observations have been used from the geothermal springs of Lake Natron, Tanzania, and compared with ASTER derived spectral composition and land surface temperature results. Published geothermal fields within the Kenyan portion of the study area have also been incorporated into this study.

Ground surface topography influence the spatial distribution of earthquake induced ground shaking. This study shows the influence of topography on seismic amplification during the 2005 Kashmir earthquake. Earth surface topography scatters and reflects seismic waves, which causes spatial variation in seismic response. We perform a 3D simulation of the 2005 Kashmir earthquake in Muzaffarabad with spectral finite element method. The moment tensor solution of the 2005 Kashmir earthquake is used as the seismic source. Our results show amplification of seismic response on ridges and de-amplification in valleys. It is found that slopes facing away from the source receive an amplified seismic response, and that 98 % of the highly damaged areas are located in the topographically amplified seismic response zone.

Permafrost-dependent landslides occur in a range of sizes and are among the most dynamic landforms in the Arctic in the warming climate. Retrogressive thaw slumps (RTSs) are enlarging landslides triggered by thawing and release of excess water from permafrost ground ice, causing smaller or larger collapses of ground surface, which in turn exposes new permafrost to rapid thawing and collapse. In this study, a preliminary assessment of previous thaw slump activity in Nordenskiöld Land area of Svalbard is made based on remote sensing digitisation of 400 slump-scar features from aerial images from the Norwegian Polar Institute (NPI). RTS properties and distribution are analysed with an emphasis on their implications for the preservation of the Svalbard’s cultural heritage (CH). Our analysis shows that the areas where RTS scars and CH co-exist in Nordenskiöld Land are, at present, limited and cover mainly areas distributed along north-west (Colesbukta, Grønfjorden, Kapp Starostin), north-east (Sassendalen and Sassenfjorden) and south-west (Van Muydenbukta) coastlines. Taking into consideration the preliminary aspect of this inventory and study, it can be stated that for now, RTS and CH sites do not have a high level of co-existence, except for eight sites which are located at less than 100 m to a RTS and one site that is located inside a currently inactive slump-scar. Further mapping of RTS will be undertaken in order to have a complete picture of these climate triggered landslides potentially threatening the Arctic CH. The results of this study, even if preliminary, can be used by local authorities and stakeholders in prioritising future documentation and mitigation measures and can thus present a powerful tool in disaster risk reduction.

Floods are frequent hydro-meteorological hazards which cause losses in many parts of the world. In hilly and mountainous environments, floods often contain sediments which are derived from mass movements and soil erosion. The deposited sediments cause significant direct damage, and indirect costs of clean-up and sediment removal. The quantification of these sediment-related costs is still a major challenge and few multi-hazard risk studies take this into account. This research is an attempt to quantify sediment deposition caused by extreme weather events in tropical regions. The research was carried out on the heavily forested volcanic island of Dominica, which was impacted by Hurricane Maria in September 2017. The intense rainfall caused soil erosion, landslides, debris flows, and flash floods resulting in a massive amount of sediments being deposited in the river channels and alluvial fan, where most settlements are located. The overall damages and losses were approximately USD 1.3 billion, USD 92 million of which relates to the cost for removing sediments. The deposition height and extent were determined by calculating the difference in elevation using pre- and post-event Unmanned Aerial Vehicle (UAV) data and additional Light Detection and Raging (LiDAR) data. This provided deposition volumes of approximately 41 and 21 (103 m3) for the two study sites. For verification, the maximum flood level was simulated using trend interpolation of the flood margins and the Digital Terrain Model (DTM) was subtracted from it to obtain flooding depth, which indicates the maximum deposition height. The sediment deposition height was also measured in the field for a number of points for verification. The methods were applied in two sites and the results were compared. We investigated the strengths and weaknesses of direct sediment observations, and analyzed the uncertainty of sediment volume estimates by DTM/DSM differencing. The study concludes that the use of pre- and post-event UAV data in heavily vegetated tropical areas leads to a high level of uncertainty in the estimated volume of sediments.

Land use and land cover change can have effect on the land by increasing/decreasing landslide susceptibility (LS) in the mountainous areas. In the southwestern hilly and mountainous part of China, land use and land cover change (LUCC) has been taking place in the recent past due to infrastructure development and increase in economic activities. These development activities can also bring negative effects: the sloping area may become susceptible to landsliding due to undercutting of slopes. The study aims at evaluating the influence of land use and land cover change on landslide susceptibility at regional scale, based on the application of Geographic Information System (GIS) and Remote Sensing (RS) technologies. Specific objective is to answer the question: which land cover/land use change poses the highest risk so that mitigation measures can be implemented in time? The Zhushan Town, Xuanen County in the southwest of China was taken as the study area and the spatial distribution of landslides was determined from visual interpretation of aerial photographs and remote sensing images, as well as field survey. Two types of land use/land cover (LUC) maps, with a time interval covering 21 years (1992–2013), were prepared: the first was obtained through the neural net classification of images acquired in 1992, the second through the object-oriented classification of images in 2002 and 2013. Landslide susceptible areas were analyzed using logistic regression models. In this process, six landslide influencing factors were chosen as the landslide susceptibility indices. Moreover, we applied a hydrologic analysis method achieving slope unit (SU) delineation to optimize the partitioning of the terrain. The results indicate that the LUCC in the region was mainly the transformation from the grassland and arable land to the forest land and the human engineering activities land (HEAL). The areas of these two kind of LUC increased by 34.3 % and 1.9 %, respectively. The comparison of landslide susceptibility maps in various periods revealed that human engineering activities was the most important factor to increase LS in this region. Such results underline that a more reasonable land use planning in the urbanization process is necessary.

The 2008 Wenchuan earthquake (China, M w 7.9) highlighted the importance of assessing and mitigating the hazards from co-seismic landslides and landslide dams. The seismic shaking triggered hundreds of thousands of landslides, about 800 of which dammed the course of rivers. To understand whether distinctive factors concurred with the river-damming events, we analyzed the spatial patterns of the river-damming landslides and the non-damming landslides separately, with reference to a number of possible controlling factors. Then, we quantified the significance of these factors using the weight of evidence method, and we used the results to perform a susceptibility assessment in a portion of the earthquake-affected region to verify the effectiveness of the method. We find that the distance to the fault surface rupture, peak ground acceleration (PGA) and lithology play a controlling role for co-seismic landslides of any type. The occurrence of river-damming landslides, rather than by a specific lithology or topography, is more related to hydrological factors, while topographic controls (slope, internal relief and terrain roughness) are more significant for the non-damming landslides.

Geomorphological and sedimentary records near the confluences of the Tana River and major tributaries draining the eastern slopes of Mt. Kenya and the Nyambeni Range, indicate impacts of Late Quaternary volcanic activity in their fluvial records. The main reconstructed event was triggered by a 366.9 ka basalt flow (40Ar/39Ar dated) which flowed along Kazita River from the Nyambeni Range blocking both Kazita River and Tana River near Kibuka Grand Falls, causing a temporary lake. Consequently, Tana River and Kazita River started to build volcanoclastic Gilbert type deltas. The preserved pro-delta sediments rich in trachytic pumice fragments display a mineralogical and age match with known Ithanguni trachytic tuffs, indicating delta build up right after a contemporary Ithanguni eruption. This trachytic eruption caused the deposition of lahars and fluvial volcaniclastic sediments in all river records draining the Eastern side of Mt. Kenya. The multiple lahars seem to be triggered by eruptions under glacial conditions (basalt age indicates MIS 10). The lava dammed lake was only short lived (estimated to have lasted only a few years to decades) and breached before a complete lake infill could occur. The current Kibuka Grand Falls can be viewed as the delayed incisional response of this lava dam breach, indicating that after >366.9 ka, Tana River is still responding and adjusting to this short-lived disruptive phase. The current Kazita River has re-incised adjacent to a MIS 4 basalt flow down into the crystalline Basement System rocks. The MIS 10 pre-volcanic sedimentary record indicates that more sediments were in the fluvial system during glacial conditions than during the interglacial conditions. An implication of our reconstruction is that the Late Quaternary fluvial record of Tana River is of only limited use to reconstruct uplift rates because reconstructed Quaternary incision rates are reflecting both volcanic disruptions as climate change trends of aridification and decreasing glaciation extents.

New broadband seismic data from Botswana and South Africa have been combined with existing data from the region to develop improved P and S wave velocity models for investigating the upper mantle structure of southern Africa. Higher craton‐like velocities are imaged beneath the Rehoboth Province and parts of the northern Okwa Terrane and the Magondi Belt, indicating that the northern edge of the greater Kalahari Craton lithosphere lies along the northern boundary of these terranes. Lower off‐craton velocities are imaged beneath the Damara‐Ghanzi‐Chobe Belt, and may in part reflect thinning of the lithosphere beneath the incipient Okavango Rift. Lower velocities are also imaged to the north and northwest of the Bushveld Complex beneath parts of the Okwa Terrane, Magondi Belt, and Limpopo Belt, indicating that cratonic upper mantle in some areas beneath these terranes may have been modified by the 2.05‐Ga Bushveld and/or 1.1‐Ga Umkondo magmatic events.

The Himalayan rivers are vulnerable to devastating flooding caused by landslides and outbreak of glacial lakes. On 7 February 2021, a deadly disaster occurred near the Rishi Ganga Hydropower Plant in the Rishi Ganga River, killing more than 100 people. During the event, a large volume of debris and broken glacial fragments flooded the Rishi Ganga River and washed away the Rishi Ganga Hydropower plant ongoing project. This study presents the impact of the Chamoli disaster on the water quality of Rishi Ganga River in upstream near Tapovan and Ganga River in downstream near Haridwar through remote sensing data. Five points have been used at different locations across the two study areas and three different indices were used such as Normalized difference water index (NDWI), Normalized difference turbidity Index (NDTI), and Normalized difference chlorophyll index (NDCI), to analyze changes in water quality. Spectral signatures and backscattering coefficients derived from Sentinel-2 Optical and Sentinel-1 Synthetic-aperture radar (SAR) data were also compared to study the changes in water quality. It was evident from the water quality indices and spectral signatures that the flood plains changed significantly. Using spectral signatures and different indices, the water level in the Chilla dam canal near Haridwar was found to decreased after the Chamoli disaster event as the flood gates were closed to stop the deposit of sediments in the canal. Results suggest changes in water quality parameters (turbidity, chlorophyll concentration, NDWI) at the five locations near the deadly site and far away at Haridwar along the Ganga River. This study is a preliminary qualitative analysis showing changes in river flood plain and water quality after the Chamoli disaster.

We present a 3D shear-wave velocity model of the southern African upper mantle developed using 30–200 s period Rayleigh waves recorded on regional seismic networks spanning the subcontinent. The model shows high velocities (∼4.7–4.8 km/s) at depths of 50–250 km beneath the Archean nucleus and several surrounding Paleoproterozoic and Mesoproterozoic terranes, placing the margin of the greater Kalahari Craton along the southern boundary of the Damara Belt and the eastern boundaries of the Gariep and Namaqua-Natal belts. At depths ≥250 km, there is little difference in velocities beneath the craton and off-craton regions, suggesting that the cratonic lithosphere extends to depths of about 200–250 km. Upper mantle velocities beneath uplifted areas of southern Africa are higher than the global average and significantly higher than beneath eastern Africa, indicating there that is little thermal modification of the upper mantle present today beneath the Southern African Plateau.

The Himalayan rivers are glacier-fed and are vulnerable to devastating flash floods caused by damming of landslides and outbreak of glacial lakes. On 7 February 2021, around 10:30 am IST, a huge block of glacier mass broke from the Nanda Ghunti glacier. It is evident from the multi-temporal satellite imageries from Planet Scope that snow dust deposited in the affected area. During the course of the event, a huge amount of debris along with broken glacial fragments flooded the Rishi Ganga river and washed away the Hydropower plants; Rishi Ganga and Tapovan, more than 71 people were killed, and about 100 people are still missing. Detailed analysis of optical and radar data has been carried out to show the impact of the rockslide, changes in the surface characteristics of the source region, flood plains of the river and water quality of the Himalayan rivers (Alaknanda and Ganga). We have used five different indices Modified Normalized difference water index (MNDWI), Normalized difference vegetation index (NDVI), Enhanced vegetation index (EVI), Normalized difference turbidity Index (NDTI), and Normalized difference chlorophyll index (NDCI), that show pronounced changes in water quality and flood plain at the four different sections of the river. The spectral reflectance and backscattering coefficients derived from high-resolution Planet scope and Sentinel 1 SAR data show characteristics behaviour of the flood plain and water quality. Further, we have also found changes in the water quality of several canals after the Chamoli disaster event as the flood gates were closed to stop the deposit of sediments in the canal.

Estimating landslide volume based on pre- and post-event elevation models without detailed field investigations is still a major challenge due to hidden failure surfaces. A MATLAB model named “SLIPFITTER” was developed to model failure surfaces using polynomial surface to fit the geometry of exposed scarps extracted from Digital Elevation Models (DEMs). To test our model, a series of analog experiments were designed to acquire data for model testing in a controlled environment. The test indicates the only option to simulate failure surfaces with only remotely sensed data, is to assume that the failure surface was outcropping at the toe of slopes. It is not possible to model landslides that do not reach the bottom of slopes without survey data. Using the assumption, the model was tested on real landslides, showing its capability of modeling for multiple landslides over an area in a single simulation, and the possibility to predict potential failure surfaces from a reported crack. The major limitations of the model include the high requirement for the input DEM quality, the incapability to consider underlying bedrock structures, and the difficulty of simulating landslides with complex movements.

We report new P and S wave velocity models of the upper mantle beneath southern Africa using data recorded on seismic stations spanning the entire subcontinent. Beneath most of the Damara Belt, including the Okavango Rift, our models show lower than average velocities (−0.8% Vp; −1.2% Vs) with an abrupt increase in velocities along the terrane's southern margin. We attribute the lower than average velocities to thinner lithosphere (~130 km thick) compared to thicker lithosphere (~200 km thick) immediately to the south under the Kalahari Craton. Beneath the Etendeka Flood Basalt Province, higher than average velocities (0.25% Vp; 0.75% Vs) indicate thicker and/or compositionally distinct lithosphere compared to other parts of the Damara Belt. In the Rehoboth Province, higher than average velocities (0.3% Vp; 0.5% Vs) suggest the presence of a microcraton, as do higher than average velocities (1.0% Vp; 1.5% Vs) under the Southern Irumide Belt. Lower than average velocities (−0.4% Vp; −0.7% Vs) beneath the Bushveld Complex and parts of the Mgondi and Okwa terranes are consistent with previous studies, which attributed them to compositionally modified lithosphere resulting from Precambrian magmatic events. There is little evidence for thermally modified upper mantle beneath any of these terranes which could provide a source of uplift for the Southern African Plateau. In contrast, beneath parts of the Irumide Belt in southern and central Zambia and the Mozambique Belt in central Mozambique, deep‐seated low velocity anomalies (−0.7% Vp; −0.8% Vs) can be attributed to upper mantle extensions of the African superplume structure.

Physical vulnerability is a challenging and fundamental issue in landslide risk assessment. Previous studies mostly focus on generalized vulnerability assessment from landslides or other types of slope failures, such as debris flow and rockfall, while the long-term damage induced by slow-moving landslides is usually ignored. In this study, a method was proposed to construct physical vulnerability curves for masonry buildings by taking the Manjiapo landslide as an example. The landslide's force acting on the buildings' foundation is calculated by applying the landslide residual-thrust calculation method. Considering four rainfall scenarios, the buildings' physical responses to the thrust are simulated in terms of potential inclination by using Timoshenko's deep-beam theory. By assuming the landslide safety factor to be landslide intensity and inclination ratio to be vulnerability, a physical vulnerability curve is fitted and the relative function is constructed by applying a Weibull distribution function. To investigate the effects of buildings' parameters that influence vulnerabilities, the length, width, height, and foundation depth and Young's modulus of the foundation are analysed. The validation results on the case building show that the physical vulnerability function can give a good result in accordance with the investigation in the field. The results demonstrate that the building length, width, and foundation depth are the three most critical factors that affect the physical vulnerability value. Also, the result shows that the higher the ratio of length to width of the building, the more serious the damage to the building. Similarly, the shallower the foundation depth is, the more serious the damage will be. We hope that the established physical vulnerability curves can serve as tools for the quantitative risk assessment of slow-moving landslides.

Roads can have a significant impact on the frequency of mass wasting events in mountainous areas. However, characterizing the extent and pervasiveness of landslides over time rarely been documented due to limitations in available data sources to consistently map such events. We monitored the evolution of a road network and assessed its effect on slope stability for a ten year window in Arhavi, Turkey. The main road construction projects run in the area are associated with a hydroelectric power plant as well as other road extension works and are clearly associated with the vast majority (90.1%) of mass movements in the area. We also notice that the overall number and size of the landslides are much larger than in the naturally-occurring comparison area. This marks a strong and negative effect of human activities on the natural course of earth surface processes. Our findings show that the damage generated by the road construction is compatible with the possible effect of a theoretical earthquake with a magnitude greater than Mw=6.0. Overall, better co- and post-construction conditions should be ensured during and after road works to mitigate the risk to local communities. We also notice a significant variation in sediment transport as a result of road construction. As a result, our study fits in the big picture of Anthropocene related changes and specifically points out at problems in mountainous areas that could undoubtedly be better managed to reduce the risk to local communities.

Hydrothermal processes modify the chemical and mineralogical composition of rock. We studied and quantified the effects of hydrothermal processes on the composition of volcanic rocks by a novel application of the Shannon entropy, which is a measure of uncertainty and commonly applied in information theory. We show here that the Shannon entropies calculated on major elemental chemical composition data and short-wave infrared (SWIR) reflectance spectra of hydrothermally altered rocks are lower than unaltered rocks with a comparable primary composition. The lowering of the Shannon entropy indicates chemical and spectral sorting during hydrothermal alteration of rocks. The hydrothermal processes described in this study present a natural mechanism for transforming energy from heat to increased order in rock. The increased order is manifest as the increased sorting of chemical elements and SWIR absorption features of the rock, and can be measured and quantified by the Shannon entropy. The results are useful for the study of hydrothermal mineral deposits, early life environments and the effects of hydrothermal processes on rocks.

The sea-level rise (SLR) resulting from climate change and flooding will threaten residents living in low-lying coastal zones in the coming decades. In this regard, evaluating social vulnerability as a significant component of flood risk reduction is necessary. Indicators, in conjunction with multi-criteria decision-making (MCDM) methods, have been recently employed to quantify social vulnerability. In the present study, an indicator-based approach was developed to assess social vulnerability to SLR and flooding in Bandar Abbas city coastal district, southern Iran. To build a social vulnerability index (SoVI) indicators were firstly categorized in, exposure, sensitivity, and adaptive capacity components. Indicators were then weighted using MCDM methods (i.e., analytical hierarchy process (AHP) and fuzzy AHP models). Subsequently, an additive weighting model was employed to produce social vulnerability maps at the block level, and under different combined flooding scenarios in 2050 and 2100. Results showed that using the fuzzy AHP does not necessarily change the ranks of indicators and components compared to the AHP model. However, the spatial extents of social vulnerability were entirely different for both models due to differences in indicators weight. It confirms that using the AHP model as a simplified method can be acceptable when determining indicators and components weight is required. Although, in the case of identifying the detailed spatial extents of social vulnerability using the fuzzy AHP might be appropriate. For all scenarios, using the AHP and fuzzy AHP model, most districts were classified as medium and low vulnerable, respectively. Moreover, the impact of SLR resulting from climate change was significantly evident in the eastern and western parts, where more districts were recognized as highly and very highly vulnerable in the worst-case scenario (S4-2100). The results of this study provide valuable comparative information that can be employed by decision-makers in coastal planning and risk reduction at local scales.

In 2017, hurricane Maria caused unprecedented damage and fatalities on the Caribbean island of Dominica. In order to ‘build back better’ and to learn from the processes causing the damage, it is important to quickly document, evaluate and map changes, both in Dominica and in other high-risk countries. This paper presents an innovative and relatively low-cost and rapid workflow for accurately quantifying geomorphological changes in the aftermath of a natural disaster. We used unmanned aerial vehicle (UAV) surveys to collect aerial imagery from 44 hurricane-affected key sites on Dominica. We processed the imagery using structure from motion (SfM) as well as a purpose-built Python script for automated processing, enabling rapid data turnaround. We also compared the data to an earlier UAV survey undertaken shortly before hurricane Maria and established ways to co-register the imagery, in order to provide accurate change detection data sets. Consequently, our approach has had to differ considerably from the previous studies that have assessed the accuracy of UAV-derived data in relatively undisturbed settings. This study therefore provides an original contribution to UAV-based research, outlining a robust aerial methodology that is potentially of great value to post-disaster damage surveys and geomorphological change analysis. Our findings can be used (1) to utilise UAV in post-disaster change assessments; (2) to establish ground control points that enable before-and-after change analysis; and (3) to provide baseline data reference points in areas that might undergo future change. We recommend that countries which are at high risk from natural disasters develop capacity for low-cost UAV surveys, building teams that can create pre-disaster baseline surveys, respond within a few hours of a local disaster event and provide aerial photography of use for the damage assessments carried out by local and incoming disaster response teams.

The 3 April 2017 Moiyabana intra-plate earthquake in central Botswana occurred in a region that, until then, had been assumed to be seismically quiet. Its location away from the East African Rift system in a Proterozoic mobile belt between Archean Cratons has raised questions on the triggering mechanism and sparked various studies investigating the crustal and mantle structure in the region, the focal mechanism and the displacement associated with the event. Aeromagnetic and magnetotelluric data indicate movement on a NW striking and NE dipping fault. However, the details of the fault geometry differ when analysing each dataset independently. The geophysical inversion results plus reconstructions of fault movement from InSAR data are all compatible with normal movement and reactivation of a previous thrust fault. An open question though is to which degree fluids are responsible for triggering the event. Here we present first results of reconciling the different available datasets in an integrated analysis. We will show an updated geophysical model of the region around the hypocenter. Such a model can help to shed light on the rupture processes during the earthquake and forms a first step to unravel the genesis of this intra-plate event.

Rayleigh wave group and phase velocity measurements obtained from ambient noise and earthquake data at 51 broadband stations were used to construct the first 3-D crustal and upper mantle shear wave velocity model of Botswana. The model shows low crustal velocities associated with the Passarge and Nosop sedimentary basins, whereas the Kaapvaal, Zimbabwe, Maltahohe, and Congo Cratons are recognized by high mantle velocities. The lowest upper mantle shear wave velocity, beneath northeastern Botswana, is associated with the southwestern branch of the East African Rift System. This low-velocity mantle anomaly appears to be linked to the crust of the Okavango Rift Zone and the location of the 3 April 2017 Mw 6.5 earthquake in central Botswana. We suggest that fluids or melt at the base of the crust from the southward continuation of the East African Rift Zone triggered the intraplate earthquake in an extensional tectonic setting.

Cultural heritage is the foundation upon which global and historical values are based on. It connects us to the legacy left by our ancestors and identifies who we are as part of the modern society. Globally and specifically in the northeastern Romania, the landscape where cultural heritage sites were built on is constantly evolving due to mass wasting processes. Among these processes, landslide and gullies can disrupt the gravitational equilibrium directly or around these sites, threatening their very existence and our capacity to pass them on to future generations. Because landsliding and gullying are stochastic processes, the use of spatial statistics has often been employed to map locations at risk. In this work, we make use of advanced spatial Bayesian statistics to model landslide and gully erosion susceptibilities, separately. And, we ultimately combine these two outputs into a unified multi-hazard susceptibility model which we cross with the known cultural heritage sites in a study area close to the city of Iasi, in Romania. Specifically, we implement a Bayesian version of a Generalized Additive Model (GAM) which assumes that the two separate landslide and gully presence/absence distributions to behave according to a Bernoulli probability distribution. Contrary to common practices in the literature, the two susceptibility models both feature fixed and random effects, including covariates acting at a latent level. We do this to also capture the unexplained but spatially coherent distribution of properties not directly included in the model. As for the properties directly expressed as covariates, our GAM features terrain attributes obtained from a LIDAR survey, in addition to land use and soil layers. The two single models outstandingly perform (AUC > 0.9) both during the calibration and validation phases. This modeling procedure ensures that the probability of occurrence of the two mass wasting processes under consideration is well estimated and therefore can be used to reliably plan conservation practices for local cultural heritage sites deemed at risk.

Summary Ground Penetrating Radar (GPR) is an electromagnetic inspection method that is widely used to help locate and assess conditions of sewer pipes. However, when operated from the ground surface, the method is not efficient in identifying early stages of void formation, which if untreated, can lead to the appearance of sinkholes. To remedy this issue, a few studies have introduced in-pipe GPR inspection systems to identify structural defects and the voids behind the sewer wall. Still, less research has focused on studying the implications of emission, propagation, and reception of electromagnetic waves in the enclosed environment of sewer pipes and their subsequent impact on the resulting radargrams. In this study, we address this matter by modeling and comparing equivalent planar (ground-surface) and cylindrical (in-pipe) operation environments. Our results indicate the differences between the radargrams of the cylindrical and planar topologies in terms of intensity, the slope of hyperbolas, and presence of horizontal reflection lines. These results encourage practical considerations for conducting in-pipe GPR surveys with respect to choosing antenna separation and target depth of the survey and tuning the parameters for hyperbolas in automatic object detection algorithms.

There is intrinsic difficulty in the investigation of the largest volume of rockfalls that is expected in an area, which lies in the small number of large events, in registrable times. The maximum credible rockfall size has been associated with the properties of the rock mass discontinuities, as they delimit detachable rock blocks, and in particular with the penetration of those discontinuities that comprise rockfall sliding planes. In highly fractured rock masses, the evaluation of the penetration remains an issue. A probabilistic methodology is proposed, to measure the penetration of potential sliding planes into the interior of a rocky slope. The main hypothesis of the method is that the sliding plane persistence is interrupted along its two directions, at the intersection with two lateral discontinuity sets, as the latter displaces the former. Due to the displacement, the sliding planes are formed by quasi-planes that contain a maximum number of spacings of the intersecting joints, hence their size is restricted. The methodology requires as an input the spacing of the intersecting joint sets. Its application to a granodiorite slope confirms that for the study site, there is a maximum volume of rockfalls, excluding the possibility of large stepped failures and rupture of rock bridges. The maximum calculated persistence for the two existing sliding planes in the study site is, respectively, 28.0 m and 48.5 m. The maximum calculated sliding plane surfaces are, accordingly, 282.5 m2 and 289.3 m2. These results are compared against the observed scar dimensions at the study site, which have been retrieved alternatively, by processing a LiDAR point cloud. The results from the two alternative sources are similar, indicating that the methodology can be efficiently used to assess the sliding plane persistence and the expected maximum rockfall magnitude at the study site.

In cold climate regions a significant fraction of annual soil erosion in agricultural land occurs during snowmelt and rain on partially frozen soils. Physically based and spatially distributed soil erosion models have proved to be good tools for understanding the processes occurring at catchment scale during rainfall erosion. However, most existing erosion models do not account for snow in a suitable way. A combination of the UEBGrid snow pack model and the LISEM erosion model was therefore used in this study. The aim was to test and validate this model combination and to assess its utility in relation to quantification and process understanding. Applying this model combination to simulate surface runoff and soil erosion showed that, in principle, it is possible to satisfactorily simulate surface runoff and observed soil erosion patterns during winter. The values for the calibration parameters were similar for the two chosen winter periods when the rainfall and snowmelt episodes occurred. However, the calibration procedure showed that the utility of this combination had several limitations. It is hoped that this study can help to improve existing models and trigger new developments in including snow pack dynamics and soil freezing and thawing in soil erosion models.

Unsafe conditions may increase the vulnerability of farmers natural hazards and reduce the capacity of farmers to prevent or recover from disaster impacts. This study aimed to investigate disruptions in cropping schedules to understand unsafe conditions that contribute to vulnerability in irrigated fields served by Ir. Djuanda (Jatiluhur) reservoir in West Java. Firstly, the deviation of ongoing cropping schedules from the official cropping calendar was evaluated using the time-series Enhanced Vegetation Index (EVI) derived from MODerate-resolution Imaging Spectroradiometer (MODIS) imageries. Secondly, reasons for disruptions in cropping schedules were explored using an in-depth interview with farmers, extension officers, and water managers and analyzed using a qualitative content analysis. Thirdly, the progression from potential causes to consequences of the disruption was identified using a Bow-Tie analysis. Unsafe conditions were identified using the result of the Bow-Tie analysis. Finally, several ways to reduce vulnerability were suggested. This study has successfully showed that cropping schedules deviate from the official cropping calendar in the study area. Reasons for disruptions in cropping schedules include economic motives, weather variabilities, geographic locations, coping strategies, farmers’ interactions, and agricultural infrastructures. The Bow-Tie analysis has visualized the progression from potential causes, disruptions in cropping schedules, to potential disaster impacts. Unsafe conditions have been identified, categorized into the dangerous locations, unsustainable farming activities, unsuitable coping strategies, fragile infrastructures, and inaccurate perceptions, have been pinpointed. Addressing unsafe conditions is likely to able to reduce vulnerability in irrigated rice fields.

This paper is focused on the analysis of landslide susceptibility for civil protection purposes. A methodology was developed and applied to support measures aiming at landslide risk mitigation. It is based on GIS and the Weight of Evidence (WofE) method, which was preferred among several other statistical approaches because it is suitable for large areas, easy to interpret and simple to program. The latter feature is important for implementing a GIS tool aimed to facilitate Civil Protection in the updating of susceptibility maps. An application of the methodology was performed in a mountainous and hilly area of the Northern Apennines (Italy) located in the Province of Modena where landslides are a critical issue in terms of civil protection due to the recurrent damages to buildings, roads and infrastructures. According to the Region Emilia-Romagna Landslide Inventory Map (RER LIM), shallow slides and earth flows are by far the most widespread mass movement types. Hence, the susceptibility assessment concerned these two types of movements. The choice of the training set, based on active landslides, took into account possible limitations of the input data. The predisposing factors were lithology, slope, curvature, Slope Position Index, aspect, land use, distance from roads. The validation was conducted through the PRC and SRC curves, and direct checking (comparison with past occurrences, multi-temporal orthophotos and field surveys). The resulting models predicted the location of landslides in an acceptable manner. One map for each type of landslides was produced and afterwards they were combined in a single document to improve their intelligibility in a civil protection framework.

For the first time, this paper develops an integrated and spatially-distributed modelling approach, linking atmospheric deposition, soil erosion and sewer transport models, to assess the transfer of traffic-related pollutants in urban areas. The modelling system is applied to a small urban catchment near Paris. Two modelling scenarios are tested by using experimentally estimated and simulated atmospheric dry deposits. Simulation results are compared with continuous measurements of water flow and total suspended solids (TSS) at the catchment outlet. The performance of water flow and TSS simulations are satisfying with the calibrated parameters; however, no significant difference can be noticed at the catchment outlet between the two scenarios due to the first flush effects. Considering the Cu, BaP and BbF contents of different particle size classes, simulated event mean concentration of each pollutant is compared with local in-situ measurements. Finally, perspectives to improve model performance and experimental techniques are discussed. An integrated and spatially-distributed air-surface-sewer model is developed.Modelling the transfer of traffic-related pollutants in urban areas.Simulations of TSS concentrations fit well with the continuous measurements.Realistic simulations of Cu, BaP and BbF are achieved at the catchment outlet.

s ERT techniques are especially promising in (semi-arid) areas with shallow and rocky soils where other methods fail to produce soil moisture maps and to obtain soil profile information. Electrical Resistivity Tomography (ERT) was performed in the Peyne catchment in southern France at four sites located on four different geological substrates. The main objective was to test the usefulness of such geoelectrical method for the assessment of spatial and temporal variability of soil water content and to demarcate subsurface horizontal zones: topsoil, active zone and bedrock. We used time lapse ERT to separate lithological variability from soil moisture content and to evaluate its potential to demarcate subsurface soil horizons. Three measurement campaigns were carried out and field data were collected using the Sting R1/IP advanced resistivity meter with one-channel receiver. We used an array of 28 electrodes with 1 m spacing, which provided reliable resistivity information to a depth of approximately 5.5 m. ERT were followed by soil sampling carried out with a hand auger, directly below the respective geoelectrical profiles. Empirical linear regression was used to determine the specific relationships between volumetric soil moisture of the field samples and electrical resistivity data obtained in situ. Our conclusions are that ERT is a useful technique for providing information on the spatial–temporal variability of water content in semi-arid areas and for reaching depths otherwise inaccessible. In this study we proposed a method to delineate three important different soil zones: topsoil, active zone, bedrock, based on resistivity data. These three layers are essential information in understanding and modelling the interaction between subsurface and vegetation water uptake, particularly in semi-arid regions.

Urban growth may intensify local flooding problems. Understanding the spatially explicit flood consequences of possible future land cover patterns contributes to inform policy for mitigating these impacts. A cellular automata model has been coupled with the openLISEM integrated flood modeling tool to simulate scenarios of urban growth and their consequent flood; the urban growth model makes use of a continuous response variable (the percentage of built-up area) and a spatially explicit simulation of supply for urban development. The models were calibrated for Upper Lubigi (Kampala, Uganda), a sub-catchment that experienced rapid urban growth during 2004–2010; this data scarce environment was chosen in part to test the model's performance with data inputs that introduced important uncertainty. The cellular automata model was validated in Nalukolongo (Kampala, Uganda). The calibrated modeling ensemble was then used to simulate urban growth scenarios of Upper Lubigi for 2020. Two scenarios, trend conditions and a policy of strict protection of existing wetlands, were simulated. The results of simulated scenarios for Upper Lubigi show how a policy of only protecting wetlands is ineffective; further, a substantial increase of flood impacts, attributable to urban growth, should be expected by 2020. The coupled models are operational with regard to the simulation of dynamic feedbacks between flood and suitability for urban growth. The tool proved useful in generating meaningful scenarios of land cover change and comparing their policy drivers as flood mitigation measures in a data scarce environment.

Flood type classification is an optimal tool to cluster floods with similar meteorological triggering conditions. Under climate change these flood types may change differently as well as new flood types develop. This paper presents a new methodology to classify flood types, particularly for use in climate change impact studies. A weather generator is coupled with a conceptual rainfall-runoff model to create long synthetic records of discharge to efficiently build an inventory with high number of flood events. Significant discharge days are classified into causal types using k-means clustering of temperature and precipitation indicators capturing differences in rainfall amount, antecedent rainfall and snow-cover and day of year. From climate projections of bias-corrected temperature and precipitation, future discharge and associated change in flood types are assessed. The approach is applied to two different Alpine catchments: the Ubaye region, a small catchment in France, dominated by rain-on-snow flood events during spring, and the larger Salzach catchment in Austria, affected more by rainfall summer/autumn flood events. The results show that the approach is able to reproduce the observed flood types in both catchments. Under future climate scenarios, the methodology identifies changes in the distribution of flood types and characteristics of the flood types in both study areas. The developed methodology has potential to be used flood impact assessment and disaster risk management as future changes in flood types will have implications for both the local social and ecological systems in the future.

Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate‐ and large‐magnitude earthquakes trigger landslides, ranging from small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake‐induced landslides and their consequences: the magnitude M 7.6 Chi‐Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.

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.

This paper shows the results of Short-Wavelength Infrared (SWIR) spectroscopy investigations of volcanic rocks sampled from low-grade metamorphic greenstone belts of the Archean Pilbara Craton in Western Australia. From the reflectance spectra a range of spectrally active minerals were identified, including chlorites, hornblende, actinolite, epidote and white micas. The rock samples were grouped into mineral assemblages based on their spectrally identified minerals and stratigraphic positions. The metamorphic amphibolite and greenschist facies could be identified from the SWIR spectroscopic data as well as three sub zones of the greenschist facies: 1) a zone containing Fe-chlorite; 2) a zone containing intermediate chlorite and epidote; and 3) a zone containing intermediate chlorite, actinolite and hornblende. Spectral parameters were calculated from the reflectance spectra to assess the metamorphic grade and zones. Plots of the depth parameters of the Fe-OH feature near 2250 nm versus the Mg-OH feature near 2390 nm differentiate the metamorphic amphibolite and greenschist facies and a transition zone between the two. The wavelength position parameter of the Mg-OH absorption feature near 2340 nm also serves to discriminate between the various metamorphic sub zones. The identification of the metamorphic grades of the volcanic sequences in greenstone belts with SWIR spectroscopy is useful for regional geological field studies, exploration for metamorphic mineral deposits hosted in the greenstone belts and the interpretation of hyperspectral remote sensing data sets covering similar types of terranes.

This study had three aims. The first was to assess the performance of the weights-of-evidence (WofE) landslide susceptibility model in areas that are very different in terms of size, geoenvironmental settings, and landslide types. The second was to test the appropriate strategies to sample the mapped landslide polygon. The final aim was to evaluate the performance of the method to changes in the landslide sample size used to train the model. The method was applied to two areas: the Fella River basin (eastern Italian Alps) containing debris flows, and Buzau County (Romanian Carpathians) with shallow landslides. The three landslide sampling strategies used were: (1) the landslide scarp centroid, (2) points populating the scarp on a 50-m grid, and (3) the entire scarp polygon. The highest success rates were obtained when sampling shallow landslides as 50-m grid-points and debris flow scarps as polygons. Prediction rates were highest when using the entire scarp polygon method for both landslide types. The sample size test using the landslide centroids showed that a sample of 104 debris flow scarps was sufficient to predict the remaining 941 debris flows in the Fella River basin, while 161 shallow landslides was the minimum required number to predict the remaining 1451 scarps in Buzau County. Below these landslide sample thresholds, model performance was too low. However, using more landslides than the threshold produced a plateau effect with little to no increase in the model performance rates.

In this paper, a study aimed to assess the rockfall hazard along a portion of the SS18 coastal road, located in the coastal area of Maratea (Basilicata Region, Southern Italy), is presented. The relevance of this study derives from the location of the study area, because the SS18 is a strategic roads in a touristic area, and, since the hazard assessment was performed in 2004 within a project financed by the Viability Regional Department of Autonomous National Company of Roads (ANAS), from the possibility to validate the results by using real rockfall events occurred after 2004. The procedure for assessing the rockfall hazard was composed of four sequential analyses: (i) geomechanical and kinematic characterization of rock mass, (ii) implementation of Romana’s (1985) Slope Mass Rating (SMR) method for identifying the potential boulder release areas (rockfall initiation areas), (iii) determination of rockfall trajectories by using a 3D numerical model (ROTOMAP), (iv) calculation and mapping of the hazard index by combining three factors, i.e., (a) lithological features of outcropping materials on rock faces, (b) kinematic compatibility defined by simulating the rockfall trajectories, and (c) spatial distribution of occurred rockfall events. Finally, the proposed methodology was validated by combining the distribution of the hazard levels along the road with the location on the SS18 of the rockfall events occurred from 2004 to 2014.

Previous prospectivity modelling for epithermal Au–Ag deposits in the Deseado Massif, southern Argentina, provided regional-scale prospectivity maps that were of limited help in guiding exploration activities within districts or smaller areas, because of their low level of detail. Because several districts in the Deseado Massif still need to be explored, prospectivity maps produced with higher detail would be more helpful for exploration in this region. We mapped prospectivity for low- and intermediate-sulfidation epithermal deposits (LISEDs) in the Deseado Massif at both regional and district scales, producing two different prospectivity models, one at regional scale and the other at district-scale. The models were obtained from two datasets of geological evidence layers by the weights-of-evidence (WofE) method. We used more deposits than in previous studies, and we applied the leave-one-out cross validation (LOOCV) method, which allowed using all deposits for training and validating the models. To ensure statistical robustness, the regional and district-scale models were selected amongst six combinations of geological evidence layers based on results from conditional independence tests. The regional-scale model (1000 m spatial resolution), was generated with readily available data, including a lithological layer with limited detail and accuracy, a clay alteration layer derived from a Landsat 5/7 band ratio, and a map of proximity to regional-scale structures. The district-scale model (100 m spatial resolution) was generated from evidence layers that were more detailed, accurate and diverse than the regional-scale layers. They were also more cumbersome to process and combine to cover large areas. The evidence layers included clay alteration and silica abundance derived from ASTER data, and a map of lineament densities. The use of these evidence layers was restricted to areas of favourable lithologies, which were derived from a geological map of higher detail and accuracy than the one used for the regional-scale prospectivity mapping. The two prospectivity models were compared and their suitability for prediction of the prospectivity in the district-scale area was determined. During the modelling process, the spatial association of the different types of evidence and the mineral deposits were calculated. Based on these results the relative importance of the different evidence layers could be determined. It could be inferred which type of geological evidence could potentially improve the modelling results by additional investigation and better representation. We conclude that prospectivity mapping for LISEDs at regional and district-scales were successfully carried out by using WofE and LOOCV methods. Our regional-scale prospectivity model was better than previous prospectivity models of the Deseado Massif. Our district-scale prospectivity model showed to be more effective, reliable and useful than the regional-scale model for mapping at district level. This resulted from the use of higher resolution evidential layers, higher detail and accuracy of the geological maps, and the application of ASTER data instead of Landsat ETM + data. District-scale prospectivity mapping could be further improved by: a) a more accurate determination of the age of mineralization relative to that of lithological units in the districts; b) more accurate and detailed mapping of the favourable units than what is currently available; c) a better understanding of the relationships between LISEDs and the geological evidence used in this research, in particular the relationship with hydrothermal clay alteration, and the method of detection of the clay minerals; and d) inclusion of other data layers, such as geochemistry and geophysics, that have not been used in this study.

Satellite rainfall products for landslide early warning prediction have been spotlighted by several researchers, in the last couple of decades. This study investigates the use of TRMM and ERA-Interim data, for the determination of rainfall thresholds and the prediction of precipitation, respectively, to be used for landslide early warning purposes at the Bogowonto catchment, Central Java, Indonesia. A landslide inventory of 218 landslides for the period of 2003–2016 was compiled, and rainfall data were retrieved for the landslide locations, as given by 6 ground stations, TRMM, and ERA-Interim data. First, rainfall data from the three different sources was compared in terms of correlation and extreme precipitation indices. Second, a procedure for the calculation of rainfall thresholds for landslide occurrence was followed consisting of four steps: i) the TRMM-based rainfall data was reconstructed for selected dates and locations characterized by landslide occurrence and non-occurrence; ii) the antecedent daily rainfall was calculated for 3, 5, 10, 15, 20 and 30 days for the selected dates and locations; iii) two-parameter daily rainfall-antecedent rainfall thresholds were calculated for the aforementioned dates; after analysis of the curves the optimum number of antecedent rainfall days was selected; and (iv) empirical rainfall thresholds for landslide occurrence were determined. The procedure was repeated for the entire landslide dataset, differentiating between forested and built-up areas, and between landslide occurrence in four temporal periods, in relation to the monsoon. The results indicated that TRMM performs well for the detection of very heavy precipitation and can be used to indicate the extreme rainfall events that trigger landslides. On the contrary, as ERA-Interim failed to detect those events, its applicability for LEWS remains limited. The 15-day antecedent rainfall was indicated to mostly affect the landslide occurrence in the area. The rainfall thresholds vary for forested and built-up areas, as well as for the beginning, middle and end of the rainy season.

In the present study, spatial and temporal patterns of rain event properties are analysed. These event properties are rain event depth, event duration, mean event rain rate, peak rain rate and the time span between two consecutive rain events which is referred to as inter-event time (IET). In addition, we assessed how rain event properties change when the period over which rainfall data is aggregated changes from 1 to 6 min and when the minimum inter-event time (MIT) changes from 30 min to 8 h. Rainfall data is obtained from a field campaign in two wet seasons of June–August (JJA) of 2007 and 2008 in Gilgel Abbay watershed that is situated at the source basin of the Upper Blue Nile River in Ethiopia. The rainfall data was automatically recorded at eight stations. The results revealed that rain event depth is more related to peak rain rate than to event duration. At the start and towards the end of the wet season, the rain events have larger depth with longer duration and longer IET than those in mid-season. Event rain rate and IET are strongly related to terrain elevation. Sekela which is on a mountain area has the shortest IET while Bahir Dar which is at the south shore of Lake Tana has the longest IET. The period over which rainfall data is aggregated significantly affected the values of rain event properties that are estimated using relatively small value (30 min) of MIT but its effect diminished when the MIT is increased to 8 h. It is shown that increasing the value of MIT has the largest effect on rain event properties of mountain stations that are characterised by high rainfall intermittency.

This thesis describes the application of time series analysis to satellite -derived thermal infrared imagery, in order to determine if spatiotemporally limited anomalies can be linked to earthquake occurrence as has been suggested by literature . For this purpose, a methodology is applied that can suppress large - scale patterns and highlight subtle localized fluctuations in hypertemporal satellite data. The methodology is tested on at- sensor longwave and midwave Thermal Infra -red (TIR) Brightness Temperature data and on TIR -derived Land Surface Temperature (LST) data. Experiments are first carried out to retrieve synthetic anomalies which are imposed in real- life datasets. The methodology is then applied for detection of volcanic activity , and the results are validated using published ground -based reports. The application of this methodology is found to facilitate utilization of longwave IR input for long -term volcanic monitoring, to complement existing hotspot detection techniques and to aid monitoring of lower temperature targets even in areas of constant activity. Finally, the methodology is applied to examine the presence of detectable localized (spatial extent up to 18225 km 2 ) increases in LST prior to twenty large, shallow, land -based earthquakes worldwide. The findings show that there is no statistically significant difference between the anomaly density detected at different distances from the earthquake, at different periods (before, during and after the earthquake), and in years with and without earthquake occurrence. It is not clear if the earthquakes have no influence on the LST registered by the satellite; if the influence exists but is overshadowed by local environmental and atmospheric influences; or if influences are linked to stress accumulation rather than stress release and therefore do not coincide with the earthquake. To clarify this, more research is required on the physical background behind the links between earthquakes and TIR anomalies which are suggested by literature.

Inventories of landslides caused by different triggering mechanisms, such as earthquakes, extreme rainfall events or anthropogenic activities, may show different characteristics in terms of distribution, causal factors and frequency-area relationships. This research aims to study such differences in landslide inventories, and the effect they have on landslide susceptibility assessment. Koshi River basin in central Himalaya was taken as study area. Detailed landslide inventories were generated based on visual interpretation of remote sensing images and field investigation for different time periods and triggering mechanisms. Maps and images from the period 1992 to 2015 were used to map 5,858 rainfall-triggered landslides and after the 2015 April 25 Gorkha earthquake, an additional 1138 co-seismic landslides were mapped. A set of topographic, geological and land cover factors were employed to analyze their correlation with different types and sizes of landslides. The results show that the frequency – area distributions of rainfall and earthquake–triggered landslides varied considerably, with the former one having a larger frequency of small landslides. Also topographic factors varied considerably for the two triggering events, with both elevation and slope angle showing significantly different patterns for earthquake-triggered and rainfall-triggered landslides. Landslides were classified into two size groups, in combination with the main triggering mechanism (rainfall- or earthquake-triggered). Susceptibility maps for different combinations of landslide size and triggering mechanism were generated using logistic regression analysis. The different triggers and sizes of landslide data were used to validate the models. The results showed that susceptible areas for small and large size rainfall- and earthquake-triggered landslides differed substantially, while susceptibility maps for different size of earthquake-triggered landslides were similar.

This study investigates consequences of future changes to the provision of ecosystem services (ES) in the Romanian Carpathians. Two 2040 forest management scenarios were compared, using two indicators to describe the gains and losses of ES. Changes in landslide regulation potential were defined as changes to landslide susceptibility. High nature value grasslands characterized biodiversity support. The business as usual scenario results in a 8% lower loss of landslide regulation potential compared to the alternative scenario. It also results in a 29% higher regional net gain of landslide regulation potential. Both scenarios result in the loss of biodiversity support due to their prevalent transition of forest expansion. This type of information is crucial for informing decision makers on the locations of potential gains and losses of future development.

Cliff erosion may be a major problem in settled areas affecting populations and producing economic and ecological losses. In this paper we present a procedure to calculate the long-term retreat rate of a cliff affected by rockfalls in the Montsec Range, Eastern Pyrenees (Spain). It is composed of low, densely fractured limestones; and the rockwall is affected by rockfalls of different sizes. The rockfall scars are clearly distinguishable by their regular boundaries and by their orange colour, which contrast with the greyish old reference surface (S0) of the cliff face. We have dated different stepped surfaces of the rockwall, including S0, using cosmogenic 36Cl. The total amount of material released by rockfall activity was calculated using a high definition point cloud of the slope face obtained with a terrestrial laser scanner (TLS). The present rockwall surface has been subtracted from the reconstructed old cliff surface. This has allowed the calculation of the total volume released by rockfalls and of the retreat rate. The latter ranges from 0.31 to 0.37 mm·a- 1. This value is of the same order of magnitude as that obtained by other researchers in neighbouring regions in Spain, having similar geology and affected by rockfalls.