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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.

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.

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.

Object-based convolutional neural networks (OCNNs) have achieved great performance in the field of land-cover and land-use classification. Studies have suggested that the generation of object convolutional positions (OCPs) largely determines the performance of OCNNs. Optimized distribution of OCPs facilitates the identification of segmented objects with irregular shapes. In this study, we propose a morphology-based binary tree sampling (BTS) method that provides a reasonable, effective, and robust strategy to generate evenly distributed OCPs. The proposed BTS algorithm consists of three major steps: 1) calculating the required number of OCPs for each object, 2) dividing a vector object into smaller sub-objects, and 3) generating OCPs based on the sub-objects. Taking the object identification in land-cover and land-use classification as a case study, we compare the proposed BTS algorithm with other competing methods. The results suggest that the BTS algorithm outperforms all other competing methods, as it yields more evenly distributed OCPs that contribute to better representation of objects, thus leading to higher object identification accuracy. Further experiments suggest that the efficiency of BTS can be improved when multi-thread technology is implemented.

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.

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.

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.

The dynamic change of frozen soil is crucial to land-surface modeling, carbon feedback studies, ground engineering (e.g., constructions), and microwave remote sensing. L-Band satellite missions Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) are currently exploited to characterize soil into freeze or thaw (FT) states. However, brightness temperatures (TB) at L-band contain more information besides the FT state, particularly over permafrost or seasonally frozen soil, which has not been explored via current retrieval algorithms. To examine the potential for L-band TB observations, we define an index called Profile-Averaged Frozen Soil Fraction (Ff) related to Diurnal Amplitude Variation (DAV) of TB (i.e., ΔTB) based on the optical depth of the frozen soil column. We evaluated Ff inferred from the 0th-order microwave transfer model with the SMAP L1c product, the ground-based European Space Agency L-Band Radiometer III (ELBARA-III) TB observations, and temperature profiles collected at the Maqu station in northeastern Tibet. While there is a clear relationship between Ff and ΔTB, no apparent link exists with the ice content fraction (Ffi) within a fixed-depth soil column. The proposed model certifies that the profile-averaged soil ice content Ff relates to the dynamic microwave penetration depth by math and field measurement. The model reproduces well ΔTB in Period Freezing but has problems in Period Thawing when melted surface water obstruct the microwave signals. Our findings can be used to exploit ΔTB between 6 am and 6 pm, as a typically overpassing time by the SMOS and SMAP satellites, for estimating Ff, which can be further applied in weather/climate forecasting and for improving land-surface modeling.

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.

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

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.

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.

The effusive eruption at Mt. Fagradalsfjall began on 19 March 2021 and it ended a period of about 800 years of volcano dormancy on the Reykjanes Peninsula. To monitor and evaluate power output of the eruption, we compiled in total 254 freely available satellite images from Terra MODIS and Landsat 8 OLI-TIRS via the Google Earth Engine platform over a six-month period. This cloud computing platform offers unique opportunities for remote sensing data collection, processing, analysis, and visualizations at a regional scale with direct access to a multi-petabyte analysis-ready data catalogue. The average radiant power from the lava during this time was 437 MW, with a maximum flux of 3253 MW. The intensity thermal power output of the 2021 Fagradalsfjall (3253 MW) is in marked contrast to radiant power observed at the 2014–2015 Holuhraun Iceland (11956 MW) where, while both eruptions also hosted active lava pools and channel, Holuhraun exhibited a much greater variability in radiant power over the same period of time. We performed Spearman correlation coefficient (SCC). Our results show a positive correlation (0.64) with radiative power from the MODVOLC system, which suggests that both results follow the same general trend. The results provide a unique temporal data set of heat flux, hosted, and processed by a cloud computing platform. This enabled the rapid assessment of eruption evolution via a cloud computing platform which can collect and process time series data within minutes.

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.

The persistence over space and time of flash flood disasters – flash floods that have caused either economical losses or loss of life or both – is a diagnostic measure of areas subjected to hydrological risk. The concept of persistence can be assessed via clustering analyses, performed here to analyze the national inventory of flash flood disasters in China that occurred in the period 1950–2015. Specifically, we investigated the spatiotemporal pattern distribution of the flash flood disasters and their clustering behavior by using both global and local methods: the first based on Ripley's K function, and the second on scan statistics. As a result, we could visualize patterns of aggregated events, estimate the cluster duration and make assumptions about their evolution over time, also with respect to the precipitation trend. Due to the large spatial (the whole Chinese territory) and temporal (66 years) scale of the dataset, we were able to capture whether certain clusters gather in specific locations and times but also whether their magnitude tends to increase or decrease. Overall, the eastern regions in China are much more subjected to flash flood disasters compared to the rest of the country. Detected clusters revealed that these phenomena predominantly occur between July and October, a period coinciding with the wet season in China. The number of detected clusters increases with time, but the associated duration drastically decreases in the recent period. This may indicate a change towards triggering mechanisms which are typical of short-duration extreme rainfall events. Finally, being flash flood disasters directly linked to precipitation and their extreme realization, we indirectly assessed whether the magnitude of the trigger itself has also varied through space and time, enabling considerations in the context of climatic change.

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.

While non-analcime zeolites are plausible mineral constituents of the Martian surface, their lack of prominent spectral characteristics in the visible to shortwave infrared wavelength region makes them difficult to identify using orbital data. This lack of detection has led to the assumption that they are limited or absent. Here we assess the capability of mapping non-analcime zeolites using different mapping techniques and present the possible reasons that could lead to limited detection of zeolites using an analog study from Paleolake Tecopa, California. We used spaceborne hyperspectral Hyperion and multispectral ASTER data to identify zeolites following X-ray Diffraction (XRD) and visible-near infrared and shortwave infrared (VNIR-SWIR) spectral analysis of collected samples containing zeolites and other associated minerals. We used four of the most common spectral mapping methods: (1) band ratios, (2) minimum noise fraction (MNF), (3) spectral angle mapper (SAM), and (4) linear spectral unmixing (LSU). We were able to map zeolite-rich tuff beds using carefully selected band indices, MNF band combinations, and the pixel spectra corresponding to a zeolite-rich abandoned quarry area. Hyperion results show less overall accuracy than the ASTER image products mostly due to the low signal-to-noise ratio of the Hyperion data. The spectral characteristics of zeolites in zeolite-rich pixels are masked by phyllosilicates (e.g., smectite) present in the ground resolution cell (GRC), making it difficult to identify zeolites in phyllosilicate-bearing deposits solely based on zeolite spectra. Therefore, the SAM product derived using the pixel spectrum of the well-exposed zeolite-rich quarry shows the highest accuracy in Hyperion image products. ASTER band ratio Band 2/Band 1, representing the ferric ion, was able to identify the zeolite-rich beds with the highest accuracies. However, most of the zeolite-rich paleolake beds were difficult to identify since they are buried by other beds, mixed with other beds due to physical weathering, or the areas of exposed beds are smaller than the GRC of the satellite. The results imply that the paucity of detected zeolites on Mars, as we see at Lake Tecopa, does not preclude their wider presence, either beneath other materials, obscured by surface dust, or mixed with more spectrally dominant phases.

Ground motion scenarios exists for most of the seismically active areas around the globe. They essentially correspond to shaking level maps at given earthquake return times which are used as reference for the likely areas under threat from future ground displacements. Being landslides in seismically actively regions closely controlled by the ground motion, one would expect that landslide susceptibility maps should change as the ground motion patterns change in space and time. However, so far, statistically-based landslide susceptibility assessments have primarily been used as time-invariant.In other words, the vast majority of the statistical models does not include the temporal effect of the main trigger in future landslide scenarios. In this work, we present an approach aimed at filling this gap, bridging current practices in the seismological community to those in the geomorphological and statistical ones. More specifically, we select an earthquake-induced landslide inventory corresponding to the 1994 Northridge earthquake and build a Bayesian Generalized Additive Model of the binomial family, featuring common morphometric and thematic covariates as well as the Peak Ground Acceleration generated by the Northridge earthquake. Once each model component has been estimated, we have run 1000 simulations for each of the 217 possible ground motion scenarios for the study area. From each batch of 1000 simulations, we have estimated the mean and 95% Credible Interval to represent the mean susceptibility pattern under a specific earthquake scenario, together with its uncertainty level. Because each earthquake scenario has a specific return time, our simulations allow to incorporate the temporal dimension into any susceptibility model, therefore driving the results toward the definition of landslide hazard. Ultimately, we also share our results in vector format – a .mif file that can be easily converted into a common shapefile –. There, we report the mean (and uncertainty) susceptibility of each 1000 simulation batch for each of the 217 scenarios.

We used magnetotelluric data from 352 sites in Botswana to derive a country-wide electrical conductivity model of the crust and upper mantle structure. A robust methodological scheme and 3D inversion were used to derive a 3D electrical conductivity model with unprecedented spatial coverage. The model results show interesting features, including the major cratonic blocks and the mobile belts in Botswana. A distinctive resistive structure was imaged in southwest Botswana, which suggests the existence of the Maltahohe microcraton as a separate cratonic unit as proposed by other studies. Furthermore, the model gives new insight into the extension of the East African Rift System to Botswana and the incipient rifting in the Okavango Rift Zone. In northern Botswana, the electrical conductivity model shows a highly conductive structure beneath the Okavango Rift Zone, which connects with a deeper conductive structure that we attribute to the East African Rift System due to its vicinity to Lake Kariba, the last surface expression of the rift system. We suggest that ascending fluids or melt from the East African Rift System causes the weakening of the lithosphere and plays a significant role in the incipient continental rifting in the Okavango Rift Zone.

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.

Mass movements such as debris flows and landslide differ in behavior due to their material properties and internal forces. Models employ generalized multi-phase flow equations to adaptively describe these complex flow types. However, models commonly assume unstructured and fragmented flow after initiation of movement. In this work, existing work on two-phase mass movement equations are extended to include a full stress-strain relationship that allows for runout of (semi-) structured fluid-solid masses. The work provides both the three-dimensional equations and depth-averaged simplifications. The equations are implemented in a hybrid Material Point Method (MPM) which allows for efficient simulation of stress-strain relationships on discrete smooth particles. Using this framework, the developed model is compared to several flume experiments of clay blocks impacting fixed obstacles. Here, both final deposit patterns and fractures compare well to simulations. Additionally, numerical tests are performed to showcase the range of dynamical behavior produced by the model. Important processes such as fracturing, fragmentation and fluid release are captured by the model. While this provides an important step towards complete mass movement models, several new opportunities arise such as ground-water flow descriptions and application to fragmenting mass movements and block-slides.

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.

Hydrothermal mineral deposits are the primary source of many mineral commodities of global importance. Since hydrothermal alteration minerals associated with the formation of these mineral deposits are active in the visible and infrared range, the analysis of spectral absorption features can be used to identify the mineralogy associated with different alteration events. Some hydrothermal events are responsible for the occurrence of mineral commodities, while other events create hydrothermal alteration unrelated to the introduction of base and precious metals. Therefore, it is crucial to develop a mineral exploration strategy to rapidly identify and map the indicator minerals linked to a mineralising event. The separation of minerals of different alteration events which are spectrally active in the same overlapping range of the spectrum, is the challenge addressed in this study. High spatial resolution airborne and laboratory-based hyperspectral images are combined to detect and visualise textures of muscovite replacing pyrophyllite in the shortwave infrared (SWIR) imaging spectroscopy survey over the Buckskin Range, the volcanic-hosted lithocap part of the Yerington porphyry district, Nevada (USA). Spectral wavelength maps in different SWIR ranges are used to map the hydrothermal alteration mineralogy at both laboratory (26 µm) and airborne (1 m) scales. The airborne spectral data define outward zoning from alunite ± pyrophyllite to muscovite characterized by variable wavelength positions of its Al-OH absorption feature. The wavelength range of 1650–1850 nm is used to differentiate zones of pyrophyllite predominance over alunite within the inner domain. The laboratory data improves the characterisation of the hydrothermal alteration mineralogy, which includes alunite, pyrophyllite, muscovite, dickite, chlorite, topaz and zunyite. The textural relationship of muscovite replacing pyrophyllite is addressed through the development of a novel spectral index, the pyrophyllite-muscovite index (PMI). The characterisation of the intergrowths of pyrophyllite and muscovite at the laboratory scale is based on two aspects: (1) the definition of pervasive versus veinlet-controlled textures and (2) a subtle shift detection in the wavelength position of the Al-OH absorption feature of muscovite from 2189 to 2195 nm. The combination of the spatial patterns with the textural relationship of the pyrophyllite-muscovite association allows the identification of areas which contain the muscovite replacement of pyrophyllite. The recognition of a late muscovite replacement of pyrophyllite suggests that advanced argillic alteration reflecting intense acid leaching is followed by late near-neutral pH magmatic-hydrothermal fluids, adding K+ and potentially other alkali elements and metals in the epithermal environment. As a result of this study, we document the hydrothermal muscovite-pyrophyllite intergrowth relationships in the study area, thus contributing to an improved understanding of the lithocap epithermal system and a better assessment of its exploration potential for Au, Ag and Cu mineralisation.

Earth Observation (EO)-based mapping of cities has great potential to detect patterns beyond the physical ones. However, EO combined with the surge of machine learning techniques to map non-physical, such as socioeconomic, aspects directly, goes to the expense of reproducibility and interpretability, hence scientific validity. In this paper, we suggest shifting the focus from the direct detection of socioeconomic status from raw images through image features, to the mapping of interpretable urban morphology of basic urban elements as an intermediate step, to which socioeconomic patterns can then be related. This shift is profound, in that, rather than abstract image features, it allows to capture the morphology of real urban objects, such as buildings and streets, and use this to then interpret other patterns, including socioeconomic ones. Because socioeconomic patterns are not derived from raw image data, the mapping of these patterns is less data demanding and more replicable. Specifically, we propose a 2-step approach: (1) extraction of fundamental urban elements from satellite imagery, and (2) derivation of meaningful urban morphological patterns from the extracted elements. We refer to this 2-step approach as “EO + Morphometrics”. Technically, EO consists of applying deep learning through a reengineered U-Net shaped convolutional neural network to publicly accessible Google Earth imagery for building extraction. Methods of urban morphometrics are then applied to these buildings to compute semantically explicit and interpretable metrics of urban form. Finally, clustering is applied to these metrics to obtain morphological patterns, or urban types. The “EO + Morphometrics” approach is applied to the city of Nairobi, Kenya, where 15 different urban types are identified. To test whether this outcome meaningfully describes current urbanization patterns, we verified whether selected types matched locally designated informal settlements. We observe that four urban types, characterized by compact and organic urban form, were recurrent in such settlements. The proposed “EO + Morphometrics” approach paves the way for the large-scale identification of interpretable urban form patterns and study of associated dynamics across any region in the world.

The development of instruments and methods to assist in methane (CH4) emissions mapping is essential to the fossil fuel industry. Early identification of local CH4 sources can benefit both petroleum hydrocarbon prospecting and environmental monitoring. Generic airborne imaging spectrometers operating in the shortwave infrared (SWIR) wavelength range (1000−2400 nm) have shown their suitability for this task. However, to date, there is no airborne scanner specifically designed for the detection of CH4 plumes. To overcome current handicaps and achieve better results at local scale, further investigation in sensor design is needed to evaluate which components can be adjusted to improve CH4 mapping with airborne sensors. Here, we focus on the evaluation of currently operational airborne imaging spectrometers for CH4 mapping in the SWIR range. Data acquired over areas with known CH4 emissions by scientific and industry-grade airborne hyperspectral sensors were examined. The research was conducted in three steps: analysis of sensor design, image processing and noise simulation. In the first step, differences in the spectral sampling between the sensors were analyzed. A reference CH4 signature from HITRAN spectral database was resampled to the spectral sampling of each airborne sensor. The center wavelength of diagnostic CH4 absorption features (identified in the convolved signatures) in relation to the position of band centers from each equipment was examined. For the image processing stage, a new CH4 index and a classic matched filtering were used to map CH4 plumes. To assess the impact of the signal-to-noise ratio (SNR) of the airborne sensors on CH4 plume mapping, white noise was added to the data to simulate images with varying SNR levels. Results demonstrated that the wavelength position of band centers is a key for CH4 mapping. The CH4 plumes could be mapped only with scientific-grade sensors, in which the band centers were closer to the center of CH4 features. Simulations with the addition of random noise demonstrated that a noisier signal is probably the reason why the industry-grade sensor tested here failed to map CH4 plumes, given that all instruments have a comparable spectral sampling. Furthermore, the simulations also demonstrated that the density of the plume has also a weight on the mapping of CH4 sources, once the image that captured the densest plume requested a higher addition of noise to be lost. The overall investigation indicates that a hyperspectral airborne sensor with bands properly positioned and scientific-grade SNR would better resolve the narrow CH4 features in the SWIR range.

Sanitary landfill is still considered as one of the most significant and least expensive methods of waste disposal. It is essential to consider environmental impacts while selecting a suitable landfill site. Thus, the site selection for sanitary landfill is a complex and time-consuming task needing an assessment of multiple criteria. In the present study, a decision support system (DSS) was prepared for selecting a landfill site in a growing urban region. This study involved two steps of analysis. The first step of analysis involved the application of spatial data to prepare the thematic maps and derive their weight. The second step employed a fuzzy multicriteria decision-making (FMCDM) technique for prioritizing the identified landfill sites. Thus, initially, the analytic hierarchy process (AHP) was used for weighting the selected criteria, while the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) was applied for addressing the uncertainty associated with decision-making and prioritizing the most suitable site. A case study was conducted in the city of Memari Municipality. The main goal of this study was the initial evaluation and acquisition of landfill candidate sites by utilizing GIS and the following decision criteria: (1) environmental criteria consisting of surface water, groundwater, land elevation, land use land cover, distance from urban residence and buildup, and distance from sensitive places; and (2) socioeconomic criteria including distance from the road, population density, and land value. For preparing the final suitability map, the integration of GIS layers and AHP was used. On output, 7 suitable landfill sites were identified which were further ranked using FTOPSIS based on expert’s views. Finally, candidate site-7 and site-2 were selected as the most suitable for proposing new landfill sites in Memari Municipality. The results from this study showed that the integration of GIS with the MCDM technique can be highly applied for site suitability. The present study will be helpful to local planners and municipal authorities for proposing a planning protocol and suitable sites for sanitary landfill in the near future. Validerad;2021;Nivå 2;2021-02-02 (alebob)

Accurate determination of clay minerals can be challenging due to the natural occurrence of interlayered phases, i.e., layers of different clay species such as illite and smectite. The overlap of peaks of the constituent minerals (e.g., illite and smectite), and the similarity of diffraction patterns when not treated with ethylene glycol, hampers identification, especially when the clay content is low. We investigated the occurrence of interlayered illite/smectite in a rock sample from Rodalquilar, Spain, using X-ray diffraction, scanning electron microscopy and reflectance spectroscopy in the short-wave infrared wavelength range. For the first time, a precise determination of interlayered I/S conducted on the extracted clay fraction treated with ethylene glycol using such different approaches was provided. X-ray diffraction results demonstrated the presence of an I/S peak at around 8.4° in the untreated fraction coupled with a peak splitting at 6.7° and 9.4° 2θ when solvated with ethylene glycol. While spectroscopy indicated the occurrence of interlayered structures as a mixture of the two constituent minerals, the results of X-ray analysis showed that the interlayered clay consisted of two discrete phases (illite and smectite). The two discrete phases were observed in both the whole rock analysis and in the extracted clay fraction. This study shows that X-ray diffraction and validation with a scanning electron microscope is a mandatory, integrating tool for detecting interlayered phases since reflectance spectroscopy alone cannot be used to differentiate between interlayered clay minerals and non-interlayered mixtures. This work highlights the limits and advantages of three sensors (X-ray diffraction, scanning electron microscopy and reflectance spectroscopy) to investigate clay mixtures and interlayering, representing a significant contribution to confidence in the interpretation of interlayered clays, this being essential in mineral exploration and prospecting.

The dominant factors in Microbial Influenced Corrosion (MIC) are hard to determine because normally several individual species and their metabolites are involved, and, moreover, different metabolites may cause opposing effects. To address this problem, the effects of individual metabolites from different species should be elucidated when at the same time other metabolites are held constant. In this study, the role is investigated of simulated organic acid metabolites, namely, acetic and L–ascorbic acids, on corrosion of geo-energy pipelines (carbon steel) in a simulated Sulfate Reducing Bacteria (SRB) environment. The SRB environment is simulated using a calcium alginate biofilm, abiotic sulfide, CO2, and NaCl brine. The electrochemical results show that both simulated organic acid metabolites accelerate corrosion in a simulated SRB environment. The results are further supported by electrochemical weight losses, kinetic corrosion activation parameters, multiple linear regression, ICP-OES, pH, and XRD. However, a comparison of electrochemical results with those published in the literature for a simulated SRB environment without acetic or L-ascorbic acid under similar experimental conditions shows that the presence of acetic in this study results in lower corrosion current densities while in presence of L-ascorbic acid results into higher corrosion current densities. This implies that acetic and L-ascorbic acids inhibit and accelerate corrosion, respectively. In addition, the results highlight that H2S is a key role of corrosion in the presence of organic acid. The results of this study are important new and novel information on the role of acetic and L-ascorbic acids in corrosion of geo-energy pipelines in the SRB environment.

Surface roughness of rocks influences the spectral shape and amplitude of thermal infrared spectra, but the relationship is poorly understood. This research aims to understand the physical processes that cause the observed spectral variation by modeling the reflectance spectra with a nonlinear transmission model. We designed a model that combines rock surface reflection with transmission through clinging fines that coat the surface. The novelty of this approach is that it does not model individual particles, but compiles different combinations of thin particle transmission and reflectance of the solid surface until it has an optimal fit with actual measured rock spectra. We use directional-hemispherical reflectance spectra of two quartz sandstones with a varying surface roughness, a transmission spectrum of a powder from one of these sandstones, and a pure mineral (kaolinite) transmission spectrum as input for the model. The model reproduces spectral amplitude and shape of the principal quartz reststrahlen doublet of three out of four measured sandstone spectra. It also correctly displays the strong bands of the quartz reststrahlen doublet. With the model we demonstrate that reflectance and transmission based modeling is a promising technique for identification and correction of spectral characteristics that result from a different surface roughness.

Hydro-morphological processes (HMP; any process in the spectrum between debris flows and flash floods) threaten human infrastructures and lives; and their effects are only expected to worsen in the context of climate change. One of the ways to limit the potential damage of HMP is to take preventive or remedial actions probabilistically knowing where and how frequently they may occur. The expected information on where and how frequently a given earth surface process may manifest itself is referred to as susceptibility. And, for the whole Chinese territory, a susceptibility model for HMP is currently not available. To address this issue, we propose a yearly space-time model consisting of a Generalized Linear Model of the binomial family. The target variable of such model is the annual presence/absence information of HMP per catchment across China, from 1985 to 2015. This information has been accessed via the Chinese catalogue of HMP, a data repository the Chinese government has activated in 195X and which is still currently in use. This binary spatio-temporal information is regressed against a set of time-invariant (catchment shape indices and terrain attributes) and time-variant (urban coverage, rainfall, vegetation density and land use) covariates. Furthermore, we include a regression constant for each of the 31 years under consideration and also a three-years aggregated information on previously occurred (and not-occurred) HMP. We consider two versions of our modeling approach, an explanatory benchmark where we fit the whole space-time HMP data, including a multiple intercept per year. Furthermore, we also extend this explanatory model into a predictive one, by considering four temporal cross-validation schemes (Forward-All, Forward-Sequence, Backward-All, and Backward-Sequence), removing the yearly multiple intercept. In the first of 31 temporal replicates, Forward-All is calibrated for 1985 and then used to predict from 1986 to 2015. In the second step, a model is calibrated for 1985 and 1986 combined and used to validate the rest of the space-time series. This is replicated up to the last model where the combined data from 1985 to 2014 is calibrated to predict the last year of the HMP presence/absence data. Forward-Sequence also moves in the same temporal direction but the sampling scheme sequentially extracts two years at a time, one for calibration and one for validation. For instance, the first step is trained for 1985 and used to predict 1986; then the second step is trained for 1986 and used to predict 1987. As for Backward-All, and Backward-Sequence, their structure is the same but the temporal direction goes from 2015 to 1985. Our explanatory model suggests that the overall number of HMP events per year has increased in the last decade and that the annual susceptibility has subsequently followed the same trend. As for the four cross-validation routines, Forward-Sequence shows excellent performance with an average AUC of 0.83, slightly better than Forward-All, Backward-All, and Backward-Sequence. From an interpretative standpoint, this implies that the best spatio-temporal prediction we obtained is associated with short-term variations of the HMP distribution and that such variations should be considered in a forward temporal direction. Furthermore, we portrayed the annual susceptibility models into 30 maps, where the south-east of China is shown to exhibit the largest variation in the spatio-temporal probability of HMP occurrence. Also, we compressed the whole spatio-temporal prediction into three summary maps. These report the mean, maximum and 95\% confidence interval of the spatio-temporal susceptibility distribution per catchment, per year. The information we present has a dual value. On the one hand, we provide a platform to interpret environmental effects on HMP at a very large scale, both spatially (the whole Chinese country) and temporally (31 years of records). On the other hand, we provide information on which catchments are more prone to experience a HMP-driven disaster. Hence, a step further would be to select the more susceptible catchment for detailed analysis where physically-based models could be tested to estimate the potentially impacted areas.

Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and the occurring landslides. Moreover, detailed inventories are crucial for the succeeding phases of landslide risk studies like susceptibility and hazard assessment. The openly available inventories differ in the quality and completeness levels. Event-based landslide inventories are created based on manual interpretation, and there can be significant differences in the mapping preferences among interpreters. To address this issue, we used two different datasets to analyze the potential of U-Net and machine learning approaches for automated landslide detection in the Himalayas. Dataset-1 is composed of five optical bands from the RapidEye satellite imagery. Dataset-2 is composed of the RapidEye optical data, and ALOS-PALSAR derived topographical data. We used a small dataset consisting of 239 samples acquired from several training zones and one testing zone to evaluate our models’ performance using the fully convolutional U-Net model, Support Vector Machines (SVM), K-Nearest Neighbor, and the Random Forest (RF). We created thirty-two different maps to evaluate and understand the implications of different sample patch sizes and their effect on the accuracy of landslide detection in the study area. The results were then compared against the manually interpreted inventory compiled using fieldwork and visual interpretation of the RapidEye satellite image. We used accuracy assessment metrics such as F1-score, Precision, Recall, and Mathews Correlation Coefficient (MCC). In the context of the Nepali Himalayas, employing RapidEye images and machine learning models, a viable patch size was investigated. The U-Net model trained with 128 × 128 pixel patch size yields the best MCC results (76.59%) with the dataset-1. The added information from the digital elevation model benefited the overall detection of landslides. However, it does not improve the model’s overall accuracy but helps differentiate human settlement areas and river sand bars. In this study, the U-Net achieved slightly better results than other machine learning approaches. Although it can depend on architecture of the U-Net model and the complexity of the geographical features in the imagery, the U-Net model is still preliminary in the domain of landslide detection. There is very little literature available related to the use of U-Net for landslide detection. This study is one of the first efforts of using U-Net for landslide detection in the Himalayas. Nevertheless, U-Net has the potential to improve further automated landslide detection in the future for varied topographical and geomorphological scenes.

In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important, as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (frequency ratio and evidence belief function) and two machine learning (ML) models (random forest and XGBoost; eXtreme Gradient Boosting) for LSM in the province of Belluno (region of Veneto, northeastern Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least “important” features by using a common threshold of 0.30 for statistical and 0.03 for ML algorithms. Conclusively, we found that removing the least important features does not impact the overall accuracy of LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least important ones, namely the aspect plan and profile curvature, topographic wetness index (TWI), topographic roughness index (TRI), and normalized difference vegetation index (NDVI) in the case of the statistical model and the plan and profile curvature, TWI, and topographic position index (TPI) for ML algorithms. This confirms that the requirement for the important conditioning factor maps can be assessed based on the physiography of the region.

This study compares the performance of three analytical methods, hyperspectral imaging in the short-wave infrared range (SWIR), X-ray powder diffraction (XRD), and electron-microprobe analysis (EMPA) for the identification of minerals frequently formed during natural and technological geothermal processes. The samples from three geothermal sites in Indonesia contain a spectrum of minerals, which are characteristic for geothermal activities (alunite, jarosite, cristobalite, tridymite) and/or are involved in scaling-forming processes in geothermal plants (barite, celestine, common opal, calcite). We show and discuss the strengths and weaknesses of the individual techniques with respect to properly identifying these minerals and to approximate their relative proportions. The SWIR camera permits an unambiguous identification of only a small selection of the geothermally relevant minerals, which restricts its employment as routine tool in the context of geothermics. XRD and EPMA are more powerful techniques and deliver complementary information. In case that SWIR inactive species occur, (b) the entire spectrum of minerals present in a sample has to be unambiguously identified, (c) amorphous substances are present, (d) accurate mineral compositions are required, and (e) detailed information on minerals occurring in low abundance and grain size is warranted, the combined use of XRD and EMPA is mandatory.

Botswana is a country with relatively low seismic activity that experienced an unexpected Mw 6.5 earthquake on 3 April 2017. Using data from the first countrywide seismic network, we established a Botswana earthquake catalog for the period January 2014 to February 2018. Two areas of elevated seismic activity were detected. The first one is the Okavango Rift Zone in northern Botswana, an area that is known to be active. The other one is associated with the 2017 mainshock and its aftershocks in central Botswana; it follows the Paleoproterozoic suture between the Limpopo Belt and the Kaapvaal Craton. Double-difference relocation of these events revealed a reactivated fault system with a northwesterly strike with the aftershocks occurring at shallower depths than the mainshock at 29 km. The focal mechanisms of the mainshock and selected aftershocks are of normal faulting type with strikes similar to the orientation of the fault system. The unidirectional rupture of the mainshock in the lower crust combined with the upward migration of the aftershocks along the Moiyabana Fault and a thin low velocity anomaly the uppermost mantle are consistent with the events being produced by eclogitization of a dry metastable granulite facies rock by fluid intrusion from the mantle in an extensional stress regime. The Okavango Rift Zone is generally interpreted as the terminus of the southwestern branch of the East African Rift System. We suggest that the recent earthquakes in central Botswana may be considered as the southern continuation of this branch.

Geostatistical models of landslide susceptibility do not usually account for thermal data, although these data are widely available, and experiments demonstrate that temperature does influence the mechanical and hydraulic behaviours of soils and rocks via a variety of thermo-hydro-mechanically coupled processes.We took the epicentral region of the 2008 Wenchuan earthquake in China as our study area, for which a rich multi-temporal inventory of landslides is available. We built a landslide susceptibility model using a generalised additive model with a slope-unit partitioning of the area (~500 km2, comprising 42 sub-catchments), and a minimal set of covariates, including the map of peak ground acceleration of the mainshock and Landsat 7 land surface temperature (LST) data retrieved from Google Earth Engine.We demonstrated that the LST relates to the decay of post-earthquake landslide activity, and in particular that warmer slopes seems to be comparatively more affected by prolonged landsliding. We also verified that LST data provided different insight from that offered by the normalised difference vegetation index (NDVI), by running our model with NDVI maps instead of LST maps. The two input maps showed little collinearity, and the variable effects of the NDVI in the model output showed less complexity compared to those of the LST. This hints at the presence of thermo-mechanical effects in slopes in addition to the known hydrological effects, the latter being associated with changes in evapotranspiration and thus in principle capturable by the NDVI.Even though studies in other regions, seismic and non-seismic, are necessary, we suggest that thermal data should be used in landslide susceptibility modelling more systematically because they could potentially improve the model results and suggest physically-based relationships influencing slope stability.

Shallow landslides of the slide-type movement represent potentially damaging events in mountain areas all over the world. These geomorphic processes are caused by a combination of predisposing factors (e.g., hillslope material), preparatory conditions (e.g., prolonged snow-melt), and triggers (e.g., heavy rainfall). Data-driven methods have been used to model shallow landslides at regional scales. Traditional approaches are mainly focused on the spatial dimension, whereas the space-time component remains a challenge.This contribution is built upon data on past landslide occurrence from 2000 to 2020 events in the province of South Tyrol, Italy (7400 km²). The inventoried information systematically relates to damage-causing and infrastructure-threatening events. The methodical procedure included an initial delineation of slope units that were subsequently replicated in time (2000 to 2020) and randomly subsampled to generate balanced distributions of landslide presence/absence observations across space and time. Different spatial static factors and cumulative daily precipitation time windows were aggregated into the mapping units. A Generalized Additive Mixed Model (GAMM) was implemented to derive statistical relationships between the different static and dynamic factors and the occurrence in space-time of shallow landslides. The resulting predictions were validated from multiple perspectives and transferred into space for different combinations of dynamic factors (e.g., triggering and preparatory precipitation, seasonal effects).The first results are promising. The exploratory analysis has revealed that from a temporal viewpoint, the best-performing model consists of a combination of preparatory and triggering factors while additionally accounting for seasonal effects. The further inclusion of the spatial static factors improved the modeling results. The developed approach shows the potential to integrate static and dynamic landslide factors for large areas by also accounting for the underlying data structure (e.g., repeated observations nested in space) and data limitations (e.g., accounting for spatial data incompleteness). The proposed method is expected to enhance the predictability of shallow landslides at multiple spatial and temporal scales and provide a better understanding of the role of the environmental processes. This study is framed within the PROSLIDE project, that has received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige.

Early Warning Systems are one of the most effective tools for reducing disaster risk, however the development of Landslide Early Warning Systems (LEWS) is complicated due to the random nature of landslide occurrence and the uncertainty in mapping the parameters that cause them. Local LEWS have been effective for known landslides, but regional scale LEWS based on rainfall thresholds have not been very effective up to now. In recent years physically-based multi-hazard models have been developed which allow to predict mass movement hazards at a local scale. However, it is still difficult to apply these in LEWS in a local scale due to the coarse resolution of rainfall estimates and the high computational modelling requirements for running such models real-time. On the other hand, machine learning approaches have been used to assess the relationship between the distribution of the landslide hazard and the catchment morphometric features.This research applies a physically-based multi-hazard model combined with machine learning to forecast the mass movement impact, based on rainfall predictions in an area in Java, Indonesia. The landslide inventory was developed using a combination of local reporting data and machine learning techniques. The integrated physically-based multi hazard model OpenLISEM is used to create a database of hazard intensity maps under various rainfall scenarios. The resulting hazard intensity maps are subsequently used to subdivide the area in homogeneous zones for which warning levels are given. Machine learning is used to query the database and extract the most likely hazard intensity map based on the rainfall prediction. The intensity is then combined with exposure information of people, buildings, transportation infrastructure and agriculture to provide impact forecasts. The output of combining physically-based models with machine learning approaches has the potential to improve the prediction of landslide impact. The method also allows to make more specific local decisions related to the actions for various levels of warning (e.g. increased vigilance, removal of resources, evacuation of people). The method is currently under development as part of an Indonesian-Netherlands collaboration project to develop a blueprint to use tailored rainfall data, in combination with empirical and physically-based hydrological and landslide models, and historical landslide data for the development of thresholds for landslides and debris flows, as the basis for early warning at settlement level, applied to several test sites in Java.

Multi-temporal landslide inventories are crucial for understanding the changing dynamics and states of activity of landslide masses. However, mapping landslides over space and time is challenging as it requires lots of time and resources to delineate landslide bodies for affected areas. With the current advances in artificial intelligence models and acquisition of very high-resolution satellite imageries, the need to map landslides not just spatially, but also temporally, has become evident. Generating multi-spatiotemporal landslide inventories can allow to improve our understanding of evolving landslides and landslide re-activations, addressing the changing susceptibilities, and the associated risks to elements-at-risk. Furthermore, as a result of having multi-temporal inventories, the temporal probability of occurrence of landslides can also be investigated with the help of envelop curves based on variables like rainfall duration, intensity, cumulative rainfall, antecedent rainfall. Therefore, in this endeavour, we have developed a model that generates multi-temporal landslide inventories for some of the most affected landslide regions by using several inventories around the world, for example, in Nepal (Gorkha earthquake of 2015). This study is the first attempt to map landslides over space and time using the state-of-the-art artificial intelligence models and gives a new perspective at mapping landslides through a temporal lens. Subsequently, the modelled outputs are utilised to assess and understand the changing dynamic behaviour of past landslides.

To understand the factors that make certain areas especially prone to landslides, statistical approaches are typically used. The interpretation of statistical results in areas characterised by complex geological and geomorphological patterns can be challenging, and this makes the understanding of the causes of landslides more difficult. In some cases, landslide inventories report information on the state of activity of landslides, adding a temporal dimension that can be beneficial in the analysis. Here, we used an inventory covering a portion of Northwestern Turkey to demonstrate that active and relict landslides (that is, landslides that occurred in the past and are now stabilised) could be related to different triggers. To do so, we built two landslide susceptibility models and observed that the spatial patterns of susceptibility were completely distinct. We found that these patterns were correlated with specific controlling factors, suggesting that active landslides are regulated by current rainfalls while relict landslides may represent a signature of past earthquakes on the landscape. The importance of this result resides in that we obtained it with a purely data-driven approach, and this was possible because the active/relict landslide classification in the inventory was accurate.

Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at https://doi.org/10.17026/dans-x6c-y7x2 (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.

Kathmandu Kyirong Highway (KKH) is one of the most strategic Sino-Nepal highways. Low-cost mitigation measures are common in Nepalese highways, however, they are not even applied sufficiently to control slope instability since the major part of this highway falls still under the category of feeder road, and thus less resources are made available for its maintenance. It is subjected to frequent landslide events in an annual basis, especially during monsoon season. The Gorkha earthquake, 2015 further mobilized substantial hillslope materials and damaged the road in several locations. The aim of this research is to access the dynamic landslide susceptibility considering pre, co and post seismic mass failures. We mapped 5,349 multi-temporal landslides of 15 years (2004–2018), using high resolution satellite images and field data, and grouped them in aforementioned three time periods. Landslide susceptibility was assessed with the application of ‘certainty factor’ (CF). Seventy percent landslides were used for susceptibility modelling and 30% for validation. The obtained results were evaluated by plotting ‘receiver operative characteristic’ (ROC) curves. The CF performed well with the ‘area under curve’ (AUC) 0.820, 0.875 and 0.817 for the success rates, and 0.809, 0.890 and 0.760 for the prediction rates for respective pre, co and post seismic landslide susceptibility. The accuracy for seismic landslide susceptibility was better than pre and post-quake ones. It might be because of the differences on completeness of the landslide inventory, which might have been possibly done better for the single event based co-seismic landslide mapping in comparison with multitemporal inventories in pre and post-quake situations. The results obtained in this study provide insights on dynamic spatial probability of landslide occurrences in the changing condition of triggering agents. This work can be a good contribution to the methodologies for the evaluation of the dynamic landslide hazard and risk, which will further help to design the efficient mitigation measures along the mountain highways.

Background Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10-14 and 50-54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings The global TFR decreased from 2.72 (95% uncertainty interval [UI] 2.66-2.79) in 2000 to 2.31 (2.17-2.46) in 2019. Global annual livebirths increased from 134.5 million (131.5-137.8) in 2000 to a peak of 139.6 million (133.0-146.9) in 2016. Global livebirths then declined to 135.3 million (127.2-144.1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2.1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27.1% (95% UI 26.4-27.8) of global livebirths. Global life expectancy at birth increased from 67.2 years (95% UI 66.8-67.6) in 2000 to 73.5 years (72.8-74.3) in 2019. The total number of deaths increased from 50.7 million (49.5-51.9) in 2000 to 56.5 million (53.7-59.2) in 2019. Under-5 deaths declined from 9.6 million (9.1-10.3) in 2000 to 5.0 million (4.3-6.0) in 2019. Global population increased by 25.7%, from 6.2 billion (6.0-6.3) in 2000 to 7.7 billion (7.5-8.0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58.6 years (56.1-60.8) in 2000 to 63.5 years (60.8-66.1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019. Interpretation Over the past 20 years, fertility rates have been dropping steadily and life expectancy has been increasing, with few exceptions. Much of this change follows historical patterns linking social and economic determinants, such as those captured by the GBD Socio-demographic Index, with demographic outcomes. More recently, several countries have experienced a combination of low fertility and stagnating improvement in mortality rates, pushing more populations into the late stages of the demographic transition. Tracking demographic change and the emergence of new patterns will be essential for global health monitoring. Copyright (C) 2020 The Author(s). Published by Elsevier Ltd.