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Housing in informal settlements often lacks construction techniques that adopt criteria of resilience to natural hazards. Smartphones are rapidly diffusing in economically developing countries. The aim of this study was to assess the current use of smartphones by the masons of the informal settlements of Iringa, Tanzania, and to identify pathways for improving their construction practices. Data were collected through a mixed method approach that includes in-depth interviews, surveys, and a focus group with masons. The results reveal that only a few masons received formal training, most of them have never interacted with a qualified engineer nor consulted trained professionals when needed. Most masons own a smartphone and they extensively use it to gather technical information from the web, transfer money through mobile payments, share images of construction details, and promote their work on social media. The broad use of smartphones shows potential for enhancing construction quality. This article presents a unique analysis of the use of smartphones in the construction of informal settlements in Tanzania, which could be extended to other countries. Based on the findings, new strategies are proposed to engage with local stakeholders and foster the exchange of technical knowledge for safer settlements via smartphones.

The role of biofilm in the Microbiologically Influenced Corrosion (MIC) of carbon steel by Sulfate Reducing Bacteria (SRB) in oil and gas exploitation, and geothermal installations is investigated. Simulated biofilm made of calcium alginate, abiotic sulfide (to mimic SRB metabolic end product H2S), CO2 (to mimic CO2 from SRB dissimilatory sulfate reduction) and simulated brine (3.0 wt% NaCl) are used to simulate the SRB environment. For reference experiments, distilled water is used instead of simulated brine. The electrochemical results show that the simulated biofilm in the reference at 120 min exposure time and in brine experiments neither inhibits nor accelerates corrosion. These results are strongly supported by corrosion kinetic adsorption parameters, statistical T-test, ICP-OES, pH, SEM-EDS and XRD. The results contradict with the existing literature on the role of biofilm and this is likely due to the presence of both H2S and CO2 as simulated SRB metabolites. Despite of this discrepancy, the obtained corrosion rates (0.25 to 1.6 mm/year) in the simulated SRB environment are comparable to published corrosion rates obtained in SRB experiments (0.20 to 1.2 mm/year). The results highlight the novelty of this research and have a direct impact on the role of other microbial metabolites on the corrosion of carbon steel.

The 2008 Wenchuan earthquake lead to various complex multi-hazard chains that included seismically triggered landslide initiation, landslide runout, river damming, dam breaching, and flooding. The modeling of the interactions between such hazardous processes is challenging due to the complexity and uncertainty. Here we present an event-based physically based model that is able to simulate multi-hazard land surface process chains within a single unified simulation. The final model is used to simulate a multi-hazard chain event in the Hongchun watershed, where co-seismic landslides led to a landslide dam and, 2 years later, a debris flow that breached the landslide dam. While most aspects of the multi-hazard chain are predicted well, the correct prediction of slope failures remains the biggest challenge. Although the results should be treated carefully, the development of such a model provides a significant progress in the applicability of multi-hazard chain simulations.

The intensity, frequency, and duration of heatwaves are increasing worldwide. Still, heatwaves are systematically underreported and underresearched across the African continent. This study examines heatwaves across Mozambique, a country highly vulnerable to a variety of climate risks yet where heatwaves have received little to no attention. A spatio-temporal analysis was conducted for five heatwave characteristics (heatwave number, frequency, duration, amplitude, and magnitude) and corresponding trends from 1983 to 2016. This was done using the remotely sensed CHIRTS-daily, which presents one of the most accurate and highest resolution (5 × 5 km) daily temperature product currently available, especially for data-scarce regions. Three heatwave definitions were analyzed and compared, which are based on (1) the 90th percentile of daily maximum temperature (TX90), (2) the 90th percentile of daily minimum temperature (TN90), and (3) the Excess Heat Factor (EHF). Results were overlayed with high-resolution population data to obtain heatwave exposure and likely potential implications. Our findings show that Mozambique has experienced many heatwaves over the past decades. On average, 2–18.6 annual heatwave days (HWF) were recorded with the longest heatwaves (HWD) lasting X- 11.5 days. More and longer heatwaves were observed in the North and along the coast of Mozambique. Heatwave magnitude (HWM) ranged from 0.3 to 6.8 °C and amplitude (HWA) from 0.8 to 11.7 °C, with highest values in South and Central Mozambique. Heatwave events, days, and duration were found to be significantly increasing (p

For more than three decades, the part of the geoscientific community studying landslides through data-driven models has focused on estimating where landslides may occur across a given landscape. This concept is widely known as landslide susceptibility. And, it has seen a vast improvement from old bivariate statistical techniques to modern deep learning routines. Despite all these advancements, no spatially-explicit data-driven model is currently capable of also predicting how large landslides may be once they trigger in a specific study area. In this work, we exploit a model architecture that has already found a number of applications in landslide susceptibility. Specifically, we opt for the use of Neural Networks. But, instead of focusing exclusively on where landslides may occur, we extend this paradigm to also spatially predict classes of landslide sizes. As a result, we keep the traditional binary classification paradigm but we make use of it to complement the susceptibility estimates with a crucial information for landslide hazard assessment. We will refer to this model as Hierarchical Neural Network (HNN) throughout the manuscript. To test this analytical protocol, we use the Nepalese area where the Gorkha earthquake induced tens of thousands of landslides on the 25th of April 2015. The results we obtain are quite promising. The component of our HNN that estimates the susceptibility outperforms a binomial Generalized Linear Model (GLM) baseline we used as benchmark. We did this for a GLM represents the most common classifier in the landslide literature. Most importantly, our HNN also suitably performed across the entire procedure. As a result, the landslide-area-class prediction returned not just a single susceptibility map, as per tradition. But, it also produced several informative maps on the expected landslide size classes. Our vision is for administrations to consult these suite of model outputs and maps to better assess the risk to local communities and infrastructure. And, to promote the diffusion of our HNN, we are sharing the data and codes in a githubsec repository in the hope that we would stimulate others to replicate similar analyses.

Many geological processes from oil recovery to underground CO2storage are affected by natural molecules adsorbed on rock surfaces. Yet, geochemical models tend to overlook their formation and stability, let alone existence. With a suite of analytical techniques, we address this "missing-link" and describe fundamental mechanisms for (i) the deposition of surface-active molecules in complex brines and oils on underground minerals and (ii) the desorption of heterogeneous sorbents and its dependence on aqueous composition. First, we show that organic and inorganic constituents of both formation water and crude oil form an organo-ionic surface layer on calcite. Primary modifiers are revealed as aqueous and nonaqueous polyaromatic molecules with polar and metal-binding functional groups and solubility characteristics of asphaltenes. Formed via π-stacking and ionic and hydrogen bonding interactions, the heterogeneous organo-ionic layer establishes a physical barrier between the mineral and ambient atmosphere/fluid, impacting the dissolution and wettability of rocks. Second, we investigate desorption of the organo-ionic layer in various brines under flow and static conditions. With chromatographic and spectroscopic methods (including Raman and sum-frequency generation), we show that the release of adsorbed material from carbonate surfaces encompasses key coupled reactions: (i) dissolution of "brine-soluble" asphaltenes, leading to relative interfacial enrichment of bulky "brine-insoluble" asphaltenes, (ii) nanoscale orientational changes of surface asphaltene assemblies, carbonate ions, and water molecules at the brine-rock interface, and (iii) dissolution and surface reconstruction of the carbonate mineral. Through these reactions, the "low-salinity effect" is uncovered as a two-stage desorption process: the initial release or selective extraction of "water-soluble" sorbents and subsequent delamination of residual "water-insoluble" asphaltenes from the dissolving mineral surface. Illuminating the surface reactions of geological minerals, we conclude that surface passivation by heterogeneous organo-ionic matter is not only ubiquitous in nature but also a key regulator of the interfacial chemistry, reactivity and wettability of underground rocks.

Rapid urbanization and land use transformation coupled with climate change impact are witnessed for development urban heat island worldwide and make it major environmental concerns for human health. National Capital Region (NCR) is one of the fast sprawling National Capital city of India. The aim of the present study is to explore the spatial changes in major biophysical parameters of city over last 20 years from 2000 to 2020 using satellite data. The spatial variation in the land surface temperature and its relation with Land use, NDVI and Built-up area were evaluated and discussed in the present work. The major changes were observed in minimum and maximum temperature of the city, in 2000 minimum temperature was 23.20 °C and maximum 34.85 °C, which increased to 26.31 and 39.92 °C in 2010; and 31.70 to 44.74 °C in 2020 . Normalized differential vegetation index (NDVI) decrease from 82 to 62%, Non-Vegetated area has reduced to 62% from 82% due to fast urbanization and land transformation in last two decades. Barren and fallow land also reduces about 27% in last two decades and built-up area also shown increasing trend . The spatial changes in biophysical parameter of the city clearly indicated that NDVI, NDBI and Land use/land cover charges are the main driving forces for development of urban heat island in the city and many health issues related to heat waves are already reported in the city along with many other negative impacts on natural landscape and resources. The observations from present work will be very useful scientific information to understand the causes of elevated land surface temperature and mitigation measures. Results could be used by policy makers to develop the suitable model for long term sustainability of the cities.

On November 14, 2016, the northeastern South Island of New Zealand was hit by the magnitude Mw 7.8 Kaikōura earthquake, which is characterized by the most complex rupturing mechanism ever recorded. The widespread landslides triggered by the earthquake make this event a great case study to revisit our current knowledge of earthquake-triggered landslides in terms of factors controlling the spatial distribution of landslides and the rapid assessment of geographic areas affected by widespread landsliding. Although the spatial and size distributions of landslides have already been investigated in the literature, a polygon-based co-seismic landslide inventory with landslide size information is still not available as of June 2021. To address this issue and leverage this large landslide event, we mapped 14,233 landslides over a total area of approximately 14,000 km2. We also identified 101 landslide dams and shared them all via an open-access repository. We examined the spatial distribution of co-seismic landslides in relation to lithologic units and seismic and morphometric characteristics. We analyzed the size statistics of these landslides in a comparative manner, by using the five largest co-seismic landslide inventories ever mapped (i.e., Chi-Chi, Denali, Wenchuan, Haiti, and Gorkha). We compared our inventory with respect to these five ones to answer the question of whether the landslides triggered by the 2016 Kaikōura earthquake are less numerous and/or share size characteristics similar to those of other strong co-seismic landslide events. Our findings show that the spatial distribution of the Kaikōura landslide event is not significantly different from those belonging to other extreme landslide events, but the average landslide size generated by the Kaikōura earthquake is relatively larger compared to some other large earthquakes (i.e., Wenchuan and Gorkha).

With the increase of frequency and intensity of heavy precipitation in the future, rainfall triggered landslides (RTL) can be one of the major threat to human life and property security. Early warning systems of natural hazards are one of the most effective measure for reducing disaster losses and risks. However, the forecast of RTL in near-real-time (NRT) is extremely difficult since the quality of NRT precipitation data is relatively poor. Quantile regression forest (QRF), a state-of-the-art statistical postprocessing method, has been proved to reduce the difference existing between NRT satellite precipitation estimates and ground-based rainfall data. When predicted rainfall maps are put side by side with raw NRT satellite product, the pattern of the first matches much more closely the locations where landslide events have been mapped in a test site in North-Eastern Turkey. This leave an optimistic perspective on the application of statistical postprocessing techniques in the field of weather science and in general for natural hazard assessment. Ideally, by correcting the continuous information in space and time provided by satellite rainfall estimates, one could create a new operational tool for landslide early warning system, not bound to the financial and deployment requirement typical of rain gauge and terrestrial radar stations.

Most shallow landslides are triggered by prolonged or short intense precipitation events. In dynamic physically-based model approaches for landslide susceptibility assessment, the input precipitation data is often derived from a single or a small number of rain gauges. However, precipitation patterns show a high variance in their spatial distribution that is insufficiently captured by standard rain gauge networks, particularly if inter-station distances are large. Spatially distributed weather radar-derived rainfall products have been used as input for physically-based landslide models to overcome the shortcomings of interpolated station measurements. However, the use of weather radar precipitation in physically-based modelling is not straightforward, since it represents an indirect measurement and thus requires pre-processing steps. With this in regard, the Integrated Nowcasting through Comprehensive Analysis (INCA) system (publicly released by GeoSphere Austria) provides historical (from 2011) hourly precipitation data at a 1 x 1 km resolution that combines weather radar data, station data and elevation data for the inclusion of elevation effects. The result is a pre-processed dataset that integrates the quantitative accuracy of station data with the spatial information provided by the radar data.In this study, we investigate whether the use of INCA precipitation data leads to improved model performance of TRIGRS compared to a conventional set-up using station data. We model slope stability in a 53 km2 sub-catchment located in South Tyrol (Italy) for an event that occurred in August 2016 with the INCA data and with precipitation data derived from a single station. The study compares the performances of the two model set-ups and their required parameter calibrations. First tests indicate that the model set-up using INCA data outperforms the station data set-up, as the spatial trend present in the INCA dataset of the modelled storm event follows the spatial trend present in the landslide inventory. In earlier studies and in a preliminary comparison with station data from South Tyrol, the historical INCA data was also shown to underestimate higher precipitation intensities, indicating that the two model set-ups require separate parameter calibrations. In future research, the calibrated model using the historical INCA dataset could be used with the nowcasting datasets from INCA to investigate if and how the INCA dataset can be used for landslide early warning systems.This study is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano (Südtirol/Alto Adige). In addition, the study also made use of the High-Performance Computing systems at the University of Innsbruck.

Spatiotemporal patterns of earth surface deformation are influenced by a combination of the geologic, topographic, seismic, anthropogenic, meteorological and climatic conditions specific to any landscape of interest. These have been mostly modelled through machine learning tools. However, these influences are yet to be explored and exploited to train interpretable data-driven models and then make predictions on the deformation one may expect in space or time. This work explored this aspect by proposing the first multivariate model dedicated to InSAR-derived deformation data. The results we obtain are promising for we suitably retrieved the signal of environmental predictors, from which we then estimated the mean line of sight velocities for a number of hillslopes affected by seismic shaking. The importance of such models resides in its potential for opening an entirely new research line for slope instability modelling.

The disaster risk community has notably shifted from a response-driven approach to making informed anticipatory action choices through impact-based forecasting (IBF). Algorithms are being developed and improved to increase impact prediction abilities, and to allow automatic triggers to reduce the reliance on human judgement. However, as complexities in modelling algorithms increase, it becomes more difficult for decision makers to interpret and explain the results. This reduces the accountability and transparency, and can lead to lower adoption of the models. Therefore, humanitarian decision-makers can benefit from a mechanism to evaluate different IBF approaches, which has not yet been developed. Through a case study of anticipatory action for tropical cyclones in the Philippines, we evaluated two very different approaches to IBF: (1) a statistical trigger model that uses a machine learning algorithm with several predictor variables, and (2) an elementary trigger model that combines damage curves and weighted overlay of vulnerability indicators, to predict the impact and prioritize areas for intervention. The models were evaluated based on their performance for damage prediction and their sensitivity to different risk indicators for Typhoon Kammuri (2019) in the Philippines. The study also proposed a way of characterising the explainability specific to an IBF model, and that gives clarity on which elements, and why, influence the results, done via a model card. To facilitate this process a prototype interactive decision portal was built, which shows decision makers the sensitivity of the results to variations in input parameters. The results show that in relative terms the elementary model performed better and would have allowed to maximise impact reduction through early action, suggesting that, for this particular case, complex was not necessarily a better choice. However, the uncertainty in both models due to limitations in the initial hazard forecast indicates that multiple models need to be evaluated for practical cases that cover different characteristics of the hazard and socio-vulnerable situations. For this, the evaluation framework we developed can be expanded across operational IBF projects.

The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at regional scale, opening new avenues for landslide prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models (GAMMs) to develop an interpretable approach that identifies seasonally dynamic precipitation conditions for shallow landslides. The model builds upon a 21-year record of landslides in South Tyrol (Italy) and separates precipitation that induced landslides from precipitation that did not. The model accounts for effects acting at four temporal scales: short-term “triggering” precipitation, medium-term “preparatory” precipitation, seasonal effects and across-year data variability. It provides relative landslide probability scores that were used to establish seasonally dynamic thresholds with optimal performance in terms of hit and false alarm rates, as well as additional thresholds related to user-defined performance scores. The GAMM shows a high predictive performance and indicates that more precipitation is required to induce a landslide in summer than in winter/spring, which can presumably be attributed mainly to vegetation and temperature effects. The discussion illustrates why the quality of input data, study design and model transparency are crucial for landslide prediction using advanced data-driven techniques.

Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping pattern in a mountainous area of Nepal through a semi-automatic workflow using Sentinel-2 A/B time-series images coupled with farmer knowledge. We identified agricultural areas through iterative self-organizing data clustering of Sentinel imagery and topographic information using a digital elevation model automatically. This agricultural area was analyzed to develop crop calendars and to track seasonal crop dynamics using rule-based methods. Finally, we computed a pixel-level crop-intensity map. In the end our results were compared to ground-truth data collected in the field and published crop calendars, with an overall accuracy of 88% and kappa coefficient of 0.83. We found variations in crop intensity and seasonal crop extension across the study area, with higher intensity in plain areas with irrigation facilities and longer fallow cycles in dry and hilly regions. The semi-automatic workflow was successfully implemented in the heterogeneous topography and is applicable to the diverse topography of the entire country, providing crucial information for mapping and monitoring crops that is very useful for the formulation of strategic agricultural plans and food security in Nepal.

Climate change is transforming the risks individuals and households face, with potentially profound socioeconomic consequences such as increased poverty, inequality, and social instability. Social protection is a policy tool that governments use to help individuals and households manage risks linked to income and livelihoods, and to achieve societal outcomes such as reducing poverty and inequality. Despite its potential as a policy response to climate change, the integration of social protection within the climate policy agenda is currently limited. While the concept of risk is key to both sectors, different understandings of the nature and scope of climate change impacts and their implications, as well as of the adequacy of social protection instruments to address them, contribute to the lack of policy and practice integration. Our goal is to bridge this cognitive gap by highlighting the potential of social protection as a policy response to climate change. Using a comprehensive climate risk lens, we first explore how climate change drives risks that are within the realm of social protection, and their implications, including likely future trends in demand for social protection. Based on this analysis, we critically review existing arguments for what social protection can do and evidence of what it currently does to manage risks arising from climate change. From the analysis, a set of reconceptualised roles emerge for social protection to strategically contribute to climate-resilient development.

Roads can have a significant impact on the frequency of mass wasting events in mountainous areas. However, characterizing the extent and pervasiveness of mass movements over time has 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 mass movements for a 11-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 mass movements are much larger than in the naturally occurring comparison area. This means that the sediment load originating from the anthropogenically induced mass movements is larger than its counterpart associated with naturally occurring landslides. Notably, this extra sediment load could cause river channel aggregation, reduce accommodation space and as a consequence, it could lead to an increase in the probability and severity of flooding along the river channel. This marks a strong and negative effect of human activities on the natural course of earth surface processes. We also compare frequency-area distributions of human-induced mass movements mapped in this study and co-seismic landslide inventories from the literature. By doing so, we aim to better understand the consequences of human effects on mass movements in a comparative manner. Our findings show that the damage generated by the road construction in terms of sediment loads to river channels is compatible with the possible effect of a theoretical earthquake with a magnitude greater than Mw = 6.0.

In the face of climate change, development and humanitarian practitioners increasingly recognize the need to anticipate and manage multiple, concurrent risks. One prominent example of this increasing focus on anticipation is the rapid growth of Forecast-based Financing (FbF), in particular within Red Cross and Red Crescent (RCRC). To evaluate how anticipatory efforts managed multiple compounding risks during the COVID-19 pandemic, we examine how 14 RCRC Societies adapted their Early Action Protocols to COVID-19. Though many National Societies successfully adapted to the onset of the additional hazard of COVID-19, we find that multi-hazard risk management can be improved by: proactively developing guidelines that enable rapid adaptation of existing plans; more flexible funding mechanisms; surge capacity to provide additional human resources; and increasing local capacity and ownership for implementation to ensure supplies, skills, and decision-making authority are available when communication or travel is restricted. These findings align with wider recommendations for improving development, humanitarian, and climate adaptation practice towards local capacity and agency. They also add urgency to broader calls for more flexible disaster financing and more practitioner-oriented investment in climate risk and multi-hazard management.

Unmanned Aerial Vehicles (UAVs) for 3D indoor mapping applications are often equipped with bulky and expensive sensors, such as LIDAR (Light Detection and Ranging) or depth cameras. The same task could be also performed by inexpensive RGB cameras installed on light and small platforms that are more agile to move in confined spaces, such as during emergencies. However, this task is still challenging because of the absence of a GNSS (Global Navigation Satellite System) signal that limits the localization (and scaling) of the UAV. The reduced density of points in feature-based monocular SLAM (Simultaneous Localization and Mapping) then limits the completeness of the delivered maps. In this paper, the real-time capabilities of a commercial, inexpensive UAV (DJI Tello) for indoor mapping are investigated. The work aims to assess its suitability for quick mapping in emergency conditions to support First Responders (FR) during rescue operations in collapsed buildings. The proposed solution only uses images in input and integrates SLAM and CNN-based (Convolutional Neural Networks) Single Image Depth Estimation (SIDE) algorithms to densify and scale the data and to deliver a map of the environment suitable for real-time exploration. The implemented algorithms, the training strategy of the network, and the first tests on the main elements of the proposed methodology are reported in detail. The results achieved in real indoor environments are also presented, demonstrating performances that are compatible with FRs’ requirements to explore indoor volumes before entering the building.

A deep neural network (DNN), evolved from a traditional artificial neural network, has been seamlessly adapted for the spatial data domain over the years. Deep learning (DL) has been widely applied for a number of applications and a variety of thematic domains. This article reports on a systematic review of methods adapted in major DNN applications with remote sensing data published between 2010 and 2020 aiming to understand the major application area, a framework for model development and the prospect of DL application in spatial data analysis. It has been found that image fusion, change detection, scene classification, image segmentation, and feature detection are the most commonly used application areas. Based on the publication in these thematic areas, a generic framework has been devised to guide a model development using DL based on the methods followed in the past. Finally, recent trends and prospects in terms of data, method, and application of deep learning with remote sensing data are discussed. The review finds that while DL-based approaches have the potential to unfold hidden information, they face challenges in selecting the most appropriate data, methods, and model parameterizations which may hinder the performance. The increasing trend of application of DL in the spatial domain is expected to leverage its strength at its optimum to the research frontiers.

Grid cells (GC) and slope units (SU) are the most common mapping units in landslide susceptibility modeling. SU-based models have recently gained popularity in the field because of the availability of user-friendly software and certain advantages over GC approaches. For example, SUs are often described as more geomorphologically meaningful, less sensitive to positionally inaccurate landslide data and more flexible in representing specific variables (e.g., binary vs. count responses). In contrast to GCs, SU sizes can vary considerably within a study area. Spatially varying mapping unit sizes may be accompanied by a spatially varying likelihood of a SU being affected by a landslide. We assume that larger SUs are more likely to be labeled as "landslide-affected" than smaller SUs, which are just as susceptible to landslides simply because of their larger spatial extent. In other words, the larger the area of investigation, the more likely a landslide can be found. This may have relevant effects on subsequent landslide susceptibility models, especially if certain predictor variables correlate with SU sizes.To our knowledge, the effects of different SU sizes on landslide susceptibility models have rarely been investigated, and no approaches to explicitly consider SU size have yet been presented. In this contribution, we use Generalized Additive Mixed Models (GAMM) to confront four different strategies for dealing with spatially varying SU sizes in landslide susceptibility modeling. The analyses focus on the provided SU-based dataset related to a part of the Umbria region in Central Italy (~4,100 km²). In the first strategy, all predisposing factors, including those directly related to SU size (i.e., SU area and distance/SU area), are used for model fitting and spatial prediction. The second strategy builds upon strategy 1, but it does not consider the size of the SUs for model fitting and spatial prediction. The third strategy demonstrates the ability of SU size to discriminate SUs with landslides from those without landslides and consists of a single-variable model with the area of the SUs as its only predictor. Then, in the fourth strategy, all predictors are used for model fitting, but the effect of SU size is averaged out from the spatial prediction (i.e., the size effect is not predicted into space, but its potentially confounding effect is isolated during the model fitting).The first tests support the assumption that larger SUs are more likely labeled as landslide-affected SUs and that associated confounding effects should be considered in landslide susceptibility modeling. We present the four strategies in terms of modeled relationships, relative variable importance, spatial prediction pattern and quantitative validation results.

Strong earthquakes not only induce co-seismic mass wasting but also exacerbates the shear strength of hillslope materials and cause higher landslide susceptibility in the subsequent years following the earthquake. Previous studies have mainly investigated post-seismic landslide activity mainly by using landslide inventories. However, landslide inventories do not provide information on deformation given by ground shaking and limit our observations to only failed hillslopes. As a consequence, we lack comprehensive, quantitative analysis revealing how hillslopes behave in post- seismic periods. Satellite-based synthetic aperture radar interferometry (InSAR) could fill this gap and provide millimeter-scale measurements of ground surface displacements that can be used to monitor hillslope deformation.InSAR also provides a rich dataset to put shed light on spatiotemporal patterns of hillslope deformation, which are influenced by a combination of static and dynamic environmental characteristics specific to any landscape of interest. However, these influences are yet to be explored and exploited to train data-driven models and make predictions on the deformation one may expect in space or time.Here we use the Persistent Scatterer Interferometry technique to monitor pre- and post- seismic hillslope deformations for the area affected by the 2017 Mw 6.9 Nyingchi, China earthquake that occurred on the 2017 18th of November 2017 earthquake. We use Sentinel-1 satellite data acquired between 2016 and 2022 to examine post-seismic hillslope evolution. Using the same dataset, we also explore developing an interpretable multivariate model dedicated to InSAR-derived hillslope deformationsOur results show that the average post-seismic hillslope deformation level in the study area is still higher than its pre-seismic counterpart approximately four and a half years after the earthquake. As for the multivariate model dedicated to InSAR-derived deformation data, the results we obtain are promising for we suitably retrieved the signal of environmental predictors, from which we then estimated the mean line of sight velocities for a number of hillslopes affected by seismic shaking.

Shallow landslides are frequently occurring hazards in mountainous landscapes all over the world. These processes are caused by a combination of static (i.e., predisposing factors: topography, material properties) and dynamic controls (i.e., preparatory and triggering factors: heavy rainfall, snow-melt). Data-driven methods have been used to model shallow landslides at regional scales, in which efforts have been taken to separately investigate the spatial component (i.e., landslide susceptibility) and temporally-varying conditions (e.g., rainfall thresholds). However, the joint assessment of shallow landslides in space and time using data-driven methods remains challenging.In the present work, we aim to predict the occurrence of precipitation-induced shallow landslides in space and time (i.e., the where and the when) within the Italian province of South Tyrol (7,400 km²). In this context, we test the added value of describing the precipitation leading to landslide occurrence as a functional predictor, in contrast to traditional approaches where precipitation is taken as a scalar predictor. We built upon hourly precipitation data from the Integrated Nowcasting through Comprehensive Analysis system (INCA, provided by Geosphere Austria) and past landslide occurrences from 2000 to 2021, which systematically relate to damage-causing landslide events. The methodical framework comprised filtering the landslide inventory, sampling landslide absences in space and time (i.e., balanced across years and months), extracting static and dynamic environmental factors (e.g., topography, lithology, land cover, and hourly precipitation), and removing trivial areas and time periods. We implemented a Functional Generalized Additive Model (FGAM) to derive statistical relationships between the different static factors as scalar predictors, the hourly precipitation preceding a potential landslide event as a functional predictor, and the occurrence in space and time of shallow landslides. The resulting predictions were assessed using cross-validation and transferred into space for different precipitation measures in order to hindcast landslide events.The results from this novel approach are expected to integrate landslide predictions in space and time for large areas by accounting for static and dynamic (i.e., hourly precipitation grids) landslide controls, seasonal effects, and the underlying data limitations (e.g., inventory incompleteness). The findings associated with this research are framed within the PROSLIDE project, which has received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige.

Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid latitudes. Conversely, these procedures have hardly been explored in peri-glacial regions, mostly because of the limited presence of human settlements and thus of the need for risk assessment. However, global warming is radically changing this situation and will change it even more in the years to come. For this reason, understanding the spatial and spatio-temporal dynamics of gemorphological processes in peri-arctic environments can be crucial to make informed decision in such unstable environments but also to shed light on what changes may follow at lower latitudes. For this reason, here we explored the use of artificially intelligent models capable of recognizing locations prone to develop retrogressive thaw slumps (RTS). These are cryospheric hazards induced by permafrost degradation and their development can negatively affect human settlements or infrastructure, change the sediment budget dynamics and release greenhouse gases. Specifically, we test a binomial Generalized Additive Modeling structure to estimate probability of RTS occurrences/development in the North sector of the Alaskan territory. The results we obtain show that our binary classifier is able to accurately recognize locations prone to RTS, in a number of goodness-of-fit and cross-validation routines. Overall, our analytical protocol has been implemented with the idea in mind of building an open source tool scripted in Python.

Landslide susceptibility assessment using data-driven models has predominantly focused on predicting where landslides may occur and not on how large they might be. The spatio-temporal evaluation of landslide susceptibility has only recently been addressed, as a basis for predicting where and when landslides might occur.The present study combines these new developments by proposing a data-driven model capable of estimating how large landslides may be, for the Taiwanese territory in a fourteen year time window. To solve this task, our model assumes that landslide sizes follow a Log-Gaussian probability distribution in space and time. Spatially the area is subdivided into 46074 slope units, with 14 annual timesteps from 2004 to 2018. Based on this subdivision, the model we implemented regressed landslide sizes against a covariate set that includes temporally static and dynamic properties. In the validation of our model, we nested a wide range of cross-validation (CV) procedures, such as a randomized 10fold-CV, a spatially constrained CV, a temporal leave-one-year-out CV, and a spatio-temporal CV. The final performance was described both numerically as well as in map form.Overall, our space-time model achieves interpretable and satisfying results. With the availability of more complete landslide inventories, both temporally and spatially, we envision that spatio-temporal landslide size prediction will become the next challenge for geomorphologists to finally address a fundamental component of the landslide hazard definition. And, because of it’s spatio-temporal nature, we also envision that it may lead to landslide simulation studies for varying climate scenarios.

Flood modelling, particularly for urban contexts, often suffers from the long computation time and detailed data requirements. Real-time forecasting requires fast, data-efficient tools. A recent break-through in rapid flood simulation has allowed us to develop www.fastflood.org, an open-source platform for near-instant fully spatial flood prediction. It uses the fast sweeping method for elevation model correction, flow accumulation, and estimation of steady-state discharge. Afterwards, a compensated partial steady state is estimated through catchment characteristics and a flood depth field is iteratively back-calculated. We will show that our method, in various real-world tests, achieves over 97% accuracy compared to traditional models in flood extent modelling, while obtaining a speed increase of over 1500 times. Together with recently published global datasets on elevation and land cover, the web-based tool allows for rapid setup and simulation of flood scenarios. Due to the speed of the method, editing of risk-reducing measures (reservoirs, barriers, channel alterations) can be carried out with direct feedback on the consequences for small to medium sized areas. Testing on flash and fluvial flood events show very good promise for early warning methods by linking the flood simulation technique with weather forecasts in real-time. Finally, by employing recent IT technologies such as web-assembly, the model can run completely on the user’s machine, allowing for a sustainable, free model.

Landslides are one of the most recurring and damaging natural hazards worldwide, with rising impacts on communities, infrastructure, and the environment. Landslide hazard, vulnerability, and risk assessments are critical for landslide mitigation, land use and developmental planning. They are, however, often lacking in complex and data-poor regions. This study proposes an integrated approach to evaluate landslide hazard, vulnerability, and risk using a range of freely available geospatial data and semi-quantitative techniques for one of the most landslide-prone areas in the Hindukush mountain ranges of northern Pakistan. Very high-resolution satellite images and their spectral characteristics are utilized to develop a comprehensive landslide inventory and predisposing factors using bi-variate models to develop a landslide susceptibility map. This is subsequently integrated with landslide-triggering factors to derive a Landslide Hazard Index map. A geospatial database of the element-at-risk data is developed from the acquired remote sensing data and extensive field surveys. It contains the building’s footprints, accompanied by typological data, road network, population, and land cover. Subsequently, it is analyzed using a spatial multi-criteria evaluation technique for vulnerability assessment and further applied in a semi-quantitative technique for risk assessment in relative risk classes. The landslide risk assessment map is classified into five classes, i.e., very low (18%), low (39.4%), moderate (26.3%), high (13.3%), and very high (3%). The developed landslide risk index map shall assist in highlighting the landslide risk hotspots and their subsequent mitigation and risk reduction.

The Svalbard Archipelago represents the northernmost place on Earth where cryospheric hazards, such as thaw slumps (TSs) and thermo-erosion gullies (TEGs) could take place and rapidly develop under the influence of climatic variations. Svalbard permafrost is specifically sensitive to rapidly occurring warming, and therefore, a deeper understanding of TSs and TEGs is necessary to understand and foresee the dynamics behind local cryospheric hazards' occurrences and their global implications. We present the latest update of two polygonal inventories where the extent of TSs and TEGs is recorded across Nordenskiöld Land (Svalbard Archipelago), over a surface of approximately 4000 km2. This area was chosen because it represents the most concentrated ice-free area of the Svalbard Archipelago and, at the same time, where most of the current human settlements are concentrated. The inventories were created through the visual interpretation of high-resolution aerial photographs as part of our ongoing effort toward creating a pan-Arctic repository of TSs and TEGs. Overall, we mapped 562 TSs and 908 TEGs, from which we separately generated two susceptibility maps using a generalised additive model (GAM) approach, under the assumption that TSs and TEGs manifest across Nordenskiöld Land, according to a Bernoulli probability distribution. Once the modelling results were validated, the two susceptibility patterns were combined into the first multi-hazard cryospheric susceptibility map of the area. The two inventories are available at https://doi.org/10.1594/PANGAEA.945348 (Nicu et al., 2022a) and https://doi.org/10.1594/PANGAEA.945395 (Nicu et al., 2022b).

As the Third Pole of the Earth and the Water Tower of Asia, the Tibetan Plateau (TP) nurtures large numbers of glacial lakes, which are sensitive to global climate change. These lakes modulate the freshwater ecosystem in the region but concurrently pose severe threats to the valley population by means of sudden glacial lake outbursts and consequent floods (GLOFs). The lack of high-resolution multi-temporal inventory of glacial lakes in TP hampers a better understanding and prediction of the future trend and risk of glacial lakes. Here, we created a multi-temporal inventory of glacial lakes in TP using a 30-year record of 42,833 satellite images (1990–2019), and we discussed their characteristics and spatio-temporal evolution over the years. Results showed that their number and area had increased by 3285 and 258.82 km2 in the last 3 decades, respectively. We noticed that different regions of the TP exhibited varying change rates in glacial lake size; most regions show a trend of expansion and increase in glacial lakes, while some regions show a trend of decreasing such as the western Pamir and the eastern Hindu Kush. The mapping uncertainty is about 17.5%, which is lower than other available datasets, thus making our inventory reliable for the spatio-temporal evolution analysis of glacial lakes in the TP. Our lake inventory data are publicly published, it can help to study climate change–glacier–glacial lake–GLOF interactions in the Third Pole and serve as input to various hydro-climatic studies.

Mapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories are rare, even using manual landslide mapping. Here, we present an innovative deep learning strategy which employs transfer learning that allows for the Attention Deep Supervision Multi-Scale U-Net model to be adapted for landslide detection tasks in new areas. The method also provides the flexibility of re-training a pretrained model to detect both rainfall- and earthquake-triggered landslides on new target areas. For the mapping, we used archived Planet Lab remote sensing images spanning a period between 2009 till 2021 with spatial resolution of 3–5 m to systematically generate MT landslide inventories. When we examined all cases, our approach provided an average F1 score of 0.8 indicating that we successfully identified the spatiotemporal occurrences of landslides. To examine the size distribution of mapped landslides we compared the frequency-area distributions of predicted co-seismic landslides with manually mapped products from the literature. Results showed a good match between calculated power-law exponents where the difference ranges between 0.04 and 0.21. Overall, this study showed that the proposed algorithm could be applied to large areas to generate polygon-based MT landslide inventories.

Small Island Developing States (SIDS) are acknowledged as particularly vulnerable to extreme climate events; however, the realities for transport infrastructure and bridges are still poorly studied. Assessing bridges in this context can be challenging due to data scarcity, a lack of local standards, and uncertainty due to climate change. While bridges are designed to connect transport networks, they also carry energy, water, and communication networks, making them critical cascading failure points worthy of special attention in terms of risk assessment and resilience measures. We explore what resilience actually means for the design and management of SIDS bridge infrastructure by applying a post disaster forensics and systems approach that is not reliant on complex methods or large amounts of data. To demonstrate the practicality of our approach, we apply it to the island of Dominica, which is regularly impacted by both tropical storms and hurricanes. Our results document the extreme conditions for infrastructure and nearby settlements and the complex interrelated physical processes that occur during these events. We reflect on the implications for design approaches for bridges under these conditions and detail specific recommendations on how the resilience of existing and new bridges can be enhanced through practical measures that are achievable, even within the constraints experienced by those managing bridge infrastructure in SIDS contexts. This work adds to the growing number of studies exploring forensic disaster investigation and systems thinking, but is the first to explore bridge resilience in SIDS.

We are investigating hydrothermal processes using airborne hyperspectral imaging, airborne geophysics, and surface field mapping in the paleo-Archean Coongan greenstone sequence in the Pilbara craton in Western Australia. The greenstone sequence is well exposed and shows laterally and vertically continuous cross-sections through ~3.4 Ga volcanic and volcano-sedimentary rocks. The rock outcrops contain abundant signs of hydrothermal activity in the Paleo-Archean. The excellent exposure, lack of significant surface weathering and vegetation cover, and preservation of primary geological structures and textures make the sequence ideal for studying Early Earth hydrothermal processes, which in this belt are linked to hydrothermal mineral deposits and early-life habitats. Preliminary results show that we can identify paleo-surfaces within the sequence in which hydrothermal discharge occurred, and different types of early-life habitats. Many strata are hydrothermally altered, and at least two episodes of hydrothermal fluid discharge have be identified. Discharge zones may be connected to kilometer long feeder dykes that crosscut the stratigraphy. The spatial relationships between deposits and hydrothermal alteration minerals suggest that some deposits are linked to the hydrothermal alteration processes and architecture. Integrating airborne geophysical and hyperspectral imagery with field data is crucial in the geological interpretation workflow and target definition for fieldwork follow-up. The different data sets each provide complementary information required for reconstruction and the geology and paleo-hydrothermal processes. The results of this ongoing study may provide fundamental insights into the early Earth processes related to deposit formation and early-life habitats.

Exploration and prioritization of potentially mineralized areas by integrating different geoscientific datasets help to manage the time and costs of an exploration project. In this study, copper exploration and prioritization of explored areas were done on the Kerman copper belt, which was formed due to the subduction of the Arabian plate under the Iranian plate. After processing geoscientific layers (geology, hydrothermal alteration zones, stream sediment geochemistry, and airborne geophysics), the analytic hierarchy process (AHP) method was used to compute the weights of geoscientific layers by a decision-making group consisting of ten local experts in copper exploration. The weight of evidence (WofE) method was used to compare and assess the fairness of the AHP weights. The AHP weights were used for the multi-criteria optimization and compromise solution (VIKOR) method to integrate the geoscientific layers and create an AHP-VIKOR copper mineralization potential map. The “v” parameter of the VIKOR method was modified by replacing the concept of mathematical expectation value with the manual assignment. The real value-area (RV-A) fractal method was used to select optimum threshold values for different AHP-VIKOR copper mineralization potential classes. The accuracy of the classified AHP-VIKOR copper mineralized potential map was assessed by creating a confusion matrix and using existing mineralization locations. The average pixel values of the copper mineralization class were extracted and sorted to rank the very high potential areas. The AHP-VIKOR ranking results were compared with recorded ore grade and ore tonnage information of each copper deposition. The results demonstrated that the AHP ranks are roughly similar to the WofE ranks, except for lithology. An accuracy of 84% for classified copper mineralization potential map with the RV-A fractal method was obtained. The used expectation value for the “v” parameter of the VIKOR method showed that the decision-making group’s opinion was more important than the individual opinions (v = 0.6). The AHP-VIKOR method identified mineralization areas, especially porphyry deposits. Also, the outcome of the AHP-VIKOR ranking showed that the currently active mines, especially porphyry deposits, have the highest priority for mining. This study showed that the AHP-VIKOR approach helps explore and rank favorable areas with high copper ore grade and tonnage characteristics.

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.

Various mechanisms are proposed to explain landslide recovery time in the time following major earthquakes. However, research on prescribing possible recovery times following an earthquake is still relatively new. This paper provides an insight into factors governing landslide recovery time, which could be considered as a step forward in predictive modeling for landslide recovery time. To accomplish this, we examined 11 earthquake-affected areas based on the characteristics of both landslide events and landslide sites associated with diverse morphologic and climatic conditions. Our analyses indicate that the dominant characteristics of post-seismic landslide mechanisms determine the recovery time. The characteristics can be identified based on: (i) the fraction of area affected by landslides (%), (ii) mean relief and its standard deviation (m), (iii) average daily accumulated precipitation (mm) and (iv) rainfall seasonality index. If there are not enough co-seismic landslide deposits or not enough relief to trigger large deposits on hillslopes, then the recovery processes are mostly controlled by new landslides caused by a strength reduction of hillslope materials. In most of the cases, this brings a relatively quick recovery process in which the majority of post-seismic landslides may happen within a year or even in a month if sufficient intense rainfalls occur soon after the earthquake. If the predisposing factors create large co-seismic landslide deposits, then remobilization of material takes the role of the dominant mechanism and recovery may take years. Overall, our analyses show that the recovery takes relatively longer if a large amount of co-seismic landslide material is deposited within a high-relief mountainous environment where precipitation rate is low and not persistent.

By photodeposition of CrOx on SrTiO3-based semiconductors doped with aliovalent Mg(II) and functionalized with Ni/NiOx catalytic nanoparticles (economically significantly more viable than commonly used Rh catalysts), an increase in apparent quantum yield (AQYs) from ∼10 to 26% in overall water splitting was obtained. More importantly, deposition of CrOx also significantly enhances the stability of Ni/NiO nanoparticles in the production of hydrogen, allowing sustained operation, even in intermittent cycles of illumination. In situ elemental analysis of the water constituents during or after photocatalysis by inductively coupled plasma mass spectrometry/optical emission spectrometry shows that after CrOx deposition, dissolution of Ni ions from Ni/NiOx-Mg:SrTiO3 is significantly suppressed, in agreement with the stabilizing effect observed, when both Mg dopant and CrOx are present. State-of-the-art electron microscopy and energy-dispersive X-ray spectroscopy (EDX) and electron energy-loss spectroscopy (EELS) analyses demonstrate that upon preparation, CrOx is photodeposited in the vicinity of several, but not all, Ni/NiOx particles. This implies the formation of a Ni-Cr mixed metal oxide, which is highly effective in water reduction. Inhomogeneities in the interfacial contact, evident from differences in contact angles between Ni/NiOx particles and the Mg:SrTiO3 semiconductor, likely affect the probability of reduction of Cr(VI) species during synthesis by photodeposition, explaining the observed inhomogeneity in the spatial CrOx distribution. Furthermore, by comparison with undoped SrTiO3, Mg-doping appears essential to provide such favorable interfacial contact and to establish the beneficial effect of CrOx. This study suggests that the performance of semiconductors can be significantly improved if inhomogeneities in interfacial contact between semiconductors and highly effective catalytic nanoparticles can be resolved by (surface) doping and improved synthesis protocols.

A first attempt of Electron microprobe (EMP) based Th-U-total Pb in situ dating of monazites in tailings from a placer deposit (quartz bearing sands) from Bangka Island, Indonesia, revealed for the first time from a single locality in Indonesia an age cluster around 231 Ma as well as two single grains with ages of 1133 Ma and 1916 Ma. The monazite ages from this study agree with detrital zircon ages and whole rock isochron ages from magmatic intrusions, which were reported from various localities all over the Malaysian peninsula, Myanmar and Thailand but not yet from Bangka Island. Perhaps intrusions in the closer vicinity, which could be the source of the monazite under study, have not been discovered so far or the sedimentary catchment area of the placer deposit is much larger than assumed so far. The existence of the monazite ages investigated in this study may also raise the question of the paleogeographic position of Bangka Island and the relative timing of sedimentation and Miocene rifting. However, the present work is an excellent example for the resistance of monazite and its Th-U-Pb system to sedimentary processes and hence is a useful tool for provenance studies and source of Rare Earth Element (REEs) recovery. An important outcome of this study is that sedimentary monazite relics often provide a wide range of unrealistic ages. Such dates may be wrongly explained in terms of recrystallization along with metasomatic process, instead these ages are only a methodic artefact because monazite relics are either not thick enough for dating or show poor surfaces with numerous cracks.

Fast and accurate landslide detection is important for landslide early warning systems. However, data available from local authorities and news reports vary in accuracy (time and location). In this work, we present a new method for identifying landslides, based on Google Earth Engine (GEE) and time-series analysis of Sentinel-2 optical satellite images. The method uses vegetation loss as a proxy for disturbance caused by earthquake-related landslides, and applies a change detection algorithm to compute the Normalized Different Vegetation Index (NDVI) and Relative Different NDVI (rdNDVI). As a test case, we applied this approach to the area of Palu, Central Sulawesi, which was hit by a major earthquake on September 28, 2018. Using time series data from 2015 to 2020, we were able to accurately capture the massive landslide in Poi Village caused by this earthquake. Using GEE had many advantages: the process is semi-automatic, fast and versatile, and the boundaries of the landslide zones can be auto-generated. In addition, the analysis does not require expensive high-resolution data. Our results demonstrate the potential of this new method to produce landslide inventories in a fast, accurate and low-cost manner.

The formation and preservation of compositional heterogeneities inside the Earth affect mantle convection patterns globally and control the long-term evolution of geochemical reservoirs. However, the distribution, nature, and size of reservoirs in the Earth’s mantle are poorly constrained. Here, we invert measurements of travel times and amplitudes of seismic waves interacting with mineralogical phase transitions at 400–700-km depth to obtain global probabilistic maps of temperature and bulk composition. We find large basalt-rich pools (up to 60% basalt fraction) surrounding the Pacific Ocean, which we relate to the segregation of oceanic crust from slabs that have been subducted since the Mesozoic. Segregation of oceanic crust from initially cold and stiff slabs may be facilitated by the presence of a weak hydrated layer in the slab or by weakening upon mineralogical transition due to grain-size reduction.

A technique for estimating clinoptilolite, montmorillonite, and epsomite mineral abundances from a reflectance spectrum of mineral mixtures using spectral deconvolution and a deep neural network is proposed. Sixty-six ternary mineral mixtures were physically prepared with

The gully erosion susceptibility literature is largely dominated by contributions focused on model comparison. This has led to prioritize certain aspects and leave others underdeveloped as compared to other natural hazard applications. For instance, in gully erosion data-driven modeling most studies use different platforms when it comes to data management, modeling and conversion into predictive maps. This in turn has limited the scope to catchment-scales. In this manuscript, we opt to propose a tool where the whole modeling procedure is unified within the same cloud computing system, allowing one to get rid of potential errors caused by input/output operations but also to extend the study areas indefinitely, as cloud data-management tools easily offer access to global data. Specifically, we present an interactive tool for susceptibility modeling in Google Earth Engine (GEE), the Susceptibility Tool for GEE (STGEE). Our tool requires few input data and makes use of the breadth of predictors’ information available in GEE. In this cloud computing environment, binary classifiers typical of susceptibility models can be called and fed with information related to mapping units and any natural hazards’ distribution over the geographic space. We tested our tool to generate susceptibility estimates for gully erosion occurrences in a study area located in Sicily (Italy). The tool we propose is equipped with a series of functions to aggregate the predictors’ information in space and time over a mapping unit of choice. Here we chose a Slope Unit partition but any polygonal structure can be chosen by the user. Once this information is derived, our tool calls for a Random Forest classifier to distinguish locations prone to gully erosion from locations where this process is not probabilistically expected to develop. This is done while providing a modeling performance overview, accessible via a separate panel. Such performance can be calculated on the basis of a exploratory analysis where all the information is used to fit a benchmark model as well as a spatial k-fold cross-validation scheme. Ultimately, the predictive function can be interactively used to generate susceptibility maps in real time, for the study area as well as any study area of interest. To promote the use of our tool, we are sharing it in a GitHub repository accessible at this link: https://github.com/giactitti/STGEE.

The 2005 Kashmir earthquake has triggered widespread landslides in the Himalayan mountains in northern Pakistan and surrounding areas, some of which are active and are still posing a significant risk. Landslides triggered by the 2005 Kashmir earthquake are extensively studied; nevertheless, spatio-temporal landslide susceptibility assessment is lacking. This can be partially attributed to the limited availability of high temporal resolution remote sensing data. We present a semi-automated technique to use the Sentinel-2 MSI data for co-seismic landslide detection, landslide activities monitoring, spatio-temporal change detection, and spatio-temporal susceptibility mapping. Time series Sentinel-2 MSI images for the period of 2016–2021 and ALOS PALSAR DEM are used for semi-automated landslide inventory map development and temporal change analysis. Spectral information combined with topographical, contextual, textural, and morphological characteristics of the landslide in Sentinel-2 images is applied for landslide detection. Subsequently, spatio-temporal landslide susceptibility maps are developed utilizing the weight of evidence statistical modeling with seven causative factors, i.e., elevation, slope, geology, aspect, distance to fault, distance to roads, and distance to streams. The results reveal that landslide occurrence increased from 2016 to 2021 and that the coverage of areas of relatively high susceptibility has increased in the study area.

To identify voids around underground concrete sewer pipes, a few studies have introduced conducting in-pipe Ground Penetrating Radar (GPR) inspection systems along the pipes. However, 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. We defined simulation scenarios where we examine the influence of longitudinal and rotational survey types, antenna frequency, air–gap between antenna and sewer wall, antenna separation, and pipe wall’s mirroring effect on the resulting radargrams. Our results encourage practical considerations for designing in-pipe GPR surveys and outline the subsequent steps for automating the in-pipe GPR surveys.

Thermal infrared remote sensing is an emerging technique to determine thermal anomalies in geothermal and volcanic areas. This study investigates the potential of thermal infrared imaging to observe geothermal prospecting and thermal activity in the Peut Sagoe volcano. The volcano has not been completely studied because it is located in a remote frontier area surrounded by dense rainforest and high terrain conditions. We used operational land imager (OLI)/thermal infrared sensor (TIRS) images of Landsat 8 acquired from 2014 to 2020 to get the actual hydrothermal mineral alteration, vegetation changes (Normalized Difference Vegetation Index; NDVI), and land surface temperature (LST) of the volcanic areas, which correlate with geothermal characteristics, such as fumarole and warm ground that exist on the surface of Peut Sagoe volcano. The combination of band ratios such as 7/5, 5/4, and 6/7 effectively estimates the hydrothermal mineral alteration in the volcanic area, which is dominated by several minerals, such as clay, iron oxide, and OH/H 2O-, SO 4-, and CO-bearing minerals. Result analyses of low NDVI and high LST values are related to the vegetation changes and thermal status of the crater area, the top of the volcano, and the warm ground on the western side of the mountain. These identities demonstrate the geothermal potential of the Peut Sagoe volcano. The LST data of OLI/TIRS images indicate increased thermal activity of the volcano from 22–26°C in 2014 to 30–35°C in 2017. However, the temperature of the volcano relatively decreased to 25°C in 2015 and 2020. In order to reduce the solar effects, the LST data from Landsat were also validated with ASTER data using a combination of night and daytime images. The results show a pattern of high thermal anomalies from 2014 – 2020 in several areas suspected of having geothermal manifestations, such as craters and hot springs, the distribution of these anomalies corresponds to LST in Landsat 8 data. Moreover, NDVI values at the potential geothermal location also reduced in 2014 and 2017 due to an increasing underground volcanic activity. Results revealed that remote sense imagery is an effective approach to identifying the geothermal potential and monitoring the thermal status of volcanos in frontier areas.

Recent conceptual geological frameworks of continental monogenetic volcanism highlight that the small magma volume eruptions, resulting volcanic geology and edifice architecture in such settings are sensitive to variations in external or environmental conditions. These conditions, along with fluctuations in magma flux, can change rapidly over short time frames and cause dramatic changes in eruption style. Understanding the drivers of transitions in explosive to effusive behaviour within the short timescales of eruptions at individual volcanic centres is essential to accurately assess volcanic hazards in continental monogenetic settings. Wiri Mountain Volcanic Complex is one of the largest and most complex volcanic centres in the active Auckland Volcanic Field. Despite the significant removal of much of the original volcanic deposits, present-day exposures and historical images provide a unique opportunity to examine the growth and evolution of the volcanic complex. Wiri Mountain deposited an initial basal tuff ring (covering an area of approximately 0.67 km2) by predominantly pyroclastic density currents, followed by at least two smaller tuff rings erupted through the outer flanks of the first, in a transition from phreatomagmatic to Strombolian eruptive style. A 90 m high central scoria cone was then produced within the initial tuff ring, partially capped by lava spatter, clastogenic lava flows and lava flows that mostly covered all tuff rings, the scoria cone, and the surrounding area. A high-resolution stratigraphic study of the well-exposed tuff ring to capping magmatic succession was conducted to determine the changes in eruptive style and their driving forces. The deposit architecture of Wiri Mountain can be described using three volcanic stratigraphic units: a basal unit comprised of tuff, lapilli tuff and tuff breccia deposits, a middle unit comprised of juvenile-rich transitional tuff deposits of black scoria ash and lapilli, red scoria and spatter, and a capping unit comprised of agglutinate and lava flow successions. Most volcanic materials were either fragmented and ejected at near-optimal scaled depth, or the transition between near-optimal and shallower/deeper depths. Wiri Mountain provides a striking example of a fissure eruption likely controlled by a pre-existing tectonic fault that was fed by relatively stable melt sources over a sustained period. We infer that despite eruption within a water-saturated coastal plain, the initial phreatomagmatic phase was overridden by subsequent explosive and effusive magmatic phases through the formation of an increasingly established conduit, thus allowing sustained magma flux and melt supply. The transitions, both gradual and rapid, from an initial phreatomagmatic to subsequent magmatic explosive and effusive phases fit well to the general understanding of eruption style changes over time from other larger and more complex volcanoes of Auckland, and elsewhere worldwide. Wiri Mountain showcases the fine balance between the external and internal conditions that control eruption style variations and govern the formation of complex monogenetic volcanoes.

Recent advances in open-source geospatial technologies in WebGIS allowed the visualization of a 3D complex environment on the web, exploiting realistic Globe reproduction of the real territorial asset. At the same time, in the field of gaming technologies, the new possibilities offered by open-source WebGL JavaScript libraries allowed the creation of Virtual Reality navigation models on the web. The integration between 3D GIS globe navigation models and VR environment navigation is a solution that offers a further level of detail in web navigation, exploiting the capabilities of web browsers in the best way. This research further contributes to this field, showing a workflow to integrate different 3D data in a VR and 3D WebGIS navigation model. The case study for this research is the new building of the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC) of Enschede (The Netherlands). This work tests the online integration of variety of 3D input data that can lead to different Levels of Details (LoD) of the buildings inside the Globe-based WebGIS platform. The developed solution works on desktop and mobile devices using the capabilities of the most common web browsers, avoiding any software installation. The result of this work is based on completely open-source solutions that offers the possibility to navigate within a 3D model, which is useful for citizens, governmental or private institutions in decision-making processes. This work represents a first step towards the ambition to generate a web Digital Twin platform to combine datasets from different sources in a unique open-source solution.

The geothermal industry has extensively used clay minerals as indicators for changes in temperature and circulation zones in geothermal systems. The latter includes marking the extent of the reservoir section and permeability within the system. Methylene blue (MeB) stain test value and geophysical resistivity data are often the primary tools to locate the reservoir section, especially in the early geothermal exploration stage. The reservoir section is marked by delineating the encompassing clay cap based on the distribution of smectite clay, indicated by a higher MeB value and the existence of a conductive layer. In this paper, we compare the MeB and magnetotelluric (MT) resistivity values with the clay minerals content of rocks, among others, smectite. Whereas the identification of clay mineral composition has typically relied on X-ray diffraction (XRD), we used three analytical methods (i.e. non-imaging infrared spectroscopy (IRS), infrared imaging spectroscopy (IRIS), and XRD). Our research used extracted clay fraction of ground cuttings and the original cuttings taken from the Rantau Dedap geothermal system in Sumatra (Indonesia). The IRS and XRD analyses were done on the same clay fraction, first on air-dried fraction and then on the ethylene glycol treated fraction, while IRIS was done on the original cuttings. The clay minerals identification from the three analytical methods showed similar results. Then, the comparison with MeB test results showed that smectite has the most effect on this test results, followed by mixtures of smectite-chlorite and smectite-illite. The MT resistivity value is a function of clay mineral types and lithology. Different types of clay are associated with varying resistivity ranges; from lowest to highest are smectite, mixtures between smectite and chlorite/illite, kandites, illite and chlorite. Comparing clay composition and MeB and MT resistivity data has given a better understanding of the reservoir location in Rantau Dedap.

Temperature is a key parameter controlling the rheology of lava flows. However, the unpredictable hazard of eruptions prevents direct measurements of hot volcanic bodies. Thus, the temperature of lava products is mostly retrieved by remote sensing techniques (ground- or satellite-based detectors) build on measuring infrared (IR) radiance. These well-established techniques are however subjected to important errors related to the poor knowledge of one of the most critical parameters, namely spectral emissivity. The aim of our study is to fill this gap by exploring the emissivity–temperature relationship of three different basalts. We performed in situ emissivity measurements at high temperatures (up to 1800 K) over a wide spectral range (350–8000 cm−1; 1.25–28.6 μm), using a non-contact IR apparatus. To unravel the complex radiative behavior of the samples with temperature, structural, chemical and textural analyses using Raman spectroscopy, XRD, DSC, SEM, EMPA and TEM were systematically performed. Our results show that spectral emissivity varies with temperature, wavenumber, and the sample degree of polymerization combined with total FeO content. Spectral emissivity is greatly affected by the crystallization of Fe-Mg-rich crystals at the micro-scale. Consequently, this study proves that spectral emissivity cannot be assumed constant for a single magmatic composition. Finally, our laboratory-measured values of spectral emissivity allowed refining the remote sensing temperature of the 2014–2015 Holuhraun eruption (Bárðarbunga, volcano) by 50 K in average. These new insights will ultimately reduce uncertainties in temperature estimates based on IR technology, information that are crucial to improve hazard assessment in volcanic crisis.

The evolution of the Sicily Channel Rift Zone (SCRZ) is thought to accommodate the regional tectonic stresses of the Calabrian subduction system. Much of the observations we have today are either limited to the surface or to the upper crust or deeper from regional seismic tomography, missing important details about the lithospheric structure and dynamics. It is unclear whether the rifting is passive from far-field extensional stresses or active from mantle upwelling beneath. We measure Rayleigh-and Love-wave phase velocities from ambient seismic noise and invert for 3-D shear-velocity and radial anisotropic models. Variations in crustal S-velocities coincide with topographic and tectonic features. The Tyrrhenian Sea has a similar to 10 km thin crust, followed by the SCRZ (similar to 20 km). The thickest crust is beneath the Apennine-Maghrebian Mountains (similar to 55 km). Areas experiencing extension and intraplate volcanism have positive crustal radial anisotropy (V-SH > V-SV); areas experiencing compression and subduction-related volcanism have negative anisotropy. The crustal anisotropy across the Channel shows the extent of the extension. Beneath the Tyrrhenian Sea, we find very low sub-Moho S-velocities. In contrast, the SCRZ has a thin mantle lithosphere underlain by a low-velocity zone. The lithosphere-asthenosphere boundary rises from 60 km depth beneath Tunisia to similar to 33 km beneath the SCRZ. Negative radial anisotropy in the upper mantle beneath the SCRZ is consistent with vertical mantle flow. We hypothesize a more active mantle upwelling beneath the rift than previously thought from an interplay between poloidal and toroidal fluxes related to the Calabrian slab, which in turn produces uplift at the surface and induces volcanism.

The Sciara del Fuoco (SdF) collapse scar at Stromboli is an active volcanic area affected by rapid morphological changes due to explosive/effusive eruptions and mass-wasting processes. The aim of this paper is to demonstrate the importance of an integrated analysis of multi-temporal remote sensing (photogrammetry, COSMO-SkyMed Synthetic Aperture Radar amplitude image) and marine geophysical data (multibeam and side scan sonar data) to characterize the main morphological, textural, and volumetric changes that occurred along the SdF slope in the 2020–2021 period. The analysis showed the marked erosive potential of the 19 May 2021 pyroclastic density current generated by a crater rim collapse, which mobilized a minimum volume of 44,000 m3 in the upper Sciara del Fuoco slope and eroded 350,000–400,000 m3 of material just considering the shallow-water setting. The analysis allowed us also to constrain the main factors controlling the emplacement of different lava flows and overflows during the monitored period. Despite the morphological continuity between the subaerial and submarine slope, textural variations in the SdF primarily depend on different processes and characteristics of the subaerial slope, the coastal area, the nearshore, and “deeper” marine areas.

Shallow landslides are frequently caused by an interplay of static predisposing factors (e.g., topography), mid-term preparatory factors (e.g., prolonged rainfall, seasonal changes of vegetation, snow melt), and short-term triggers (e.g., heavy rainfall). For large-area assessments, statistical analyses and data-driven approaches are often used to model landslide susceptibility based on spatial environmental variables or to derive critical landslide-triggering rainfall conditions through the definition of empirical rainfall thresholds. Attempts to integrate the spatial and temporal domains in the context of quantitative regional-scale landslide prediction are still rare.This contribution focuses on the landslide-prone area of South Tyrol, northern Italy (7,400 km²) and presents a novel data-driven modelling procedure that integrates spatial predisposing factors and dynamic preparatory and triggering factors to predict the probability of landslide occurrence in space and time. The approach is based on time-stamped landslide inventory data from 2000 to 2021, high-resolution gridded daily precipitation observations for the same period, and a set of relevant static environmental variables (e.g., including topographic indices, lithology). Data preparation included an initial filtering of rainfall-induced landslide presence observations and a rule-based stratified sampling of landslide absence observations at landslide locations and at non-landslide locations. Cross-validation was implemented in the model developing stage to select optimal time windows to represent pre-landslide preparatory and triggering cumulative rainfall conditions. Modelling was based on a binomial Hierarchical Generalized Additive Model (HGAM) that considered the non-linear influence and interactions (i.e., via tensor products) of static and dynamic environmental variables on landslide occurrence (presence vs. absence) while simultaneously accounting for the nested data structure (i.e., multiple considerations of each location) and seasonal effects. The study also considered different biases inherent in the input data, such as the known underrepresentation of landslide data in locations far from infrastructure or potential reporting biases across years, by averaging-out associated random effect variables. The results were validated quantitatively (spatial and temporal cross-validation) and qualitatively (geomorphic plausibility) and visualized as maps and probability surface plots.The assessment and validation confirmed the high generalizability and predictive performance of the model. A closer look at the derived relationships allowed to uncover the effects of predisposing, preparatory and triggering factors on landslide occurrence as well as the associated season-dependent variations. From our perspective, this new approach represents a good compromise between the high model flexibility for landslide prediction purposes and the high interpretability for understanding the underlying relationships.The research leading to these results is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige.