This research proposes an artificial intelligence (AI)-powered digital twin framework for highway slope stability risk monitoring and prediction. For highway slope stability, a digital twin replicates the geological and structural conditions of highway slopes while continuously integrating real-time monitoring data to refine and enhance slope modeling. The framework employs instance segmentation and a random forest model to identify embankments and slopes with high landslide susceptibility scores. Additionally, artificial neural network (ANN) models are trained on historical drilling data to predict 3D subsurface soil type point clouds and groundwater depth maps. The USCS soil classification-based machine learning model achieved an accuracy score of 0.8, calculated by dividing the number of correct soil class predictions by the total number of predictions. The groundwater depth regression model achieved an RMSE of 2.32. These predicted values are integrated as input parameters for seepage and slope stability analyses, ultimately calculating the factor of safety (FoS) under predicted rainfall infiltration scenarios. The proposed methodology automates the identification of embankments and slopes using sub-meter resolution Light Detection and Ranging (LiDAR)-derived digital elevation models (DEMs) and generates critical soil properties and pore water pressure data for slope stability analysis. This enables the provision of early warnings for potential slope failures, facilitating timely interventions and risk mitigation.

The Hindukush region in Northwest Pakistan is a mountainous area that often faces natural disasters, such as landslides, flash floods, glacial lake outbursts, and debris flow, that alter the landscape and damage property. This study focused on the Chitral area of the Hindukush region to assess the landslide distribution and susceptibility using field observations and factor analysis. Nine landslide causative factors were selected and weighted using Geographic Information System (GIS)-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) techniques. The factors included slope, aspect, rainfall, land cover, lithology, seismicity, distance to faults, streams, and roads. Landslide susceptibility maps were generated and classified into five categories: very high, high, moderate, low, and very low. Various landslides were observed in the field comprising debris flow, debris slide, soil erosion, and rockfall. Rockfall in the study area indicates active seismicity in the Hindukush region. Furthermore, the area under the curve method validated the results, which gave 0.80 for FR and 0.73 for AHP. The results showed that most of the landslides in the study area were caused by steep slopes of mountains, followed by precipitation. The high landslide susceptibility zones in the study area matched well with the field-based landslide collections, which showed the reliability of the mapping methods. These findings can help plan and implement measures in the Hindukush region to reduce the risk and impact of landslides, such as early warning systems, slope stabilization, land use regulation, and evacuation plans.

On 16 December 2021, the Eastern Bays region of Banks Peninsula, Canterbury, was affected by a high intensity rainfall event which triggered >1300 landslides. Landslides triggered by the event caused widespread damage to agricultural land and infrastructure, which cut off road access to several communities. A landslide inventory has been developed to examine factors that have contributed to the distribution of slope failures in this region. Generally, landslides triggered were shallow earthflows in loess and loess-derived soils largely confined to slopes 26 degrees-35 degrees within steepened valley systems formed by erosion of the underlying volcanics. Landsliding was also absent at high elevations (> 600 m) southwest of the main ridge line and along the summit edges of the valleys due to sparse distribution of loessial deposits in this area. Three deep-seated slope failures were observed in highly weathered volcanics and were associated with the day lighting of spring flow. This event highlights the complexity of landslide hazard in Banks Peninsula, and the influence of soil distribution and the underlying complex hydrogeology on landslide occurrence.

Atmospheric Brown Carbon (BrC) with strong wavelength-dependence light-absorption ability can significantly affect radiative forcing. Highly resolved emission inventories with lower uncertainties are important premise and essential in scientifically evaluating impacts of emissions on air quality, human health and climate change. This study developed a bottom-up inventory of primary BrC from combustion sources in China from 1960 to 2016 with a spatial resolution at 0.1 degrees x 0.1 degrees, based on compiled emission factors and detailed activity data. The primary BrC emission in China was about 593 Gg (500-735 Gg as interquartile range) in 2016, contributing to 7% (5%-8%) of a previously estimated global total BrC emission. Residential fuel combustion was the largest source of primary BrC in China, with the contribution of 67% as the national average but ranging from 25% to 99% among different provincial regions. Significant spatial disparities were also observed in the relative shares of different fuel types. Coal combustion contribution varied from 8% to 99% across different regions. Heilongjiang and North China Plain had high emissions of primary BrC. Generally, on the national scale, spatial distribution of BrC emission density per area was aligned with the population distribution. Primary BrC emission from combustion sources in China have been declined since a peak of similar to 1300 Gg in 1980, but the temporal trends were distinct in different sectors. The high-resolution inventory developed here enables radiative forcing simulations in future atmospheric models so as to promote better understanding of carbonaceous aerosol impacts in the Earth's climate system and to develop strategies achieving co-benefits of human health protection and climate change.

German coastal areas are often protected from flood events by a primary sea dike line of more than 1,200 km. Many transition areas, such as the change of surface covering materials and other dike elements such as stairs, fences, or ramps at intermittent locations, characterize the stretch of this sea dike line. During storm surges and wave overtopping, the onset of damage, especially dike cover erosion, is often initiated at these transitions due to locally disturbed flow characteristics, increased loads, and reduced strength at the interface. An in-depth understanding of damage initiation and building stock conditions along coastlines as a foundational element of a flood cycle is essential in order to accurately assess existing defense structures, both deterministically and probabilistically. Thus, the present study is motivated to examine the variety of transition areas on the sea dikes along the German coasts, for further assessment of probability of their damage and failure. A novel remote inventory was elaborated manually, based on satellite images for a length of 998 km along the German North Sea and 123 km along the German Baltic Sea coast and estuaries, and it shows the spatial distribution and frequency of such transitions on sea dikes. During additional on-site investigations at different locations at the coast, detailed information about design variants of dike elements as well as damage to transitions were recorded and reported systematically. The results of the on-site investigations allow the development of a damage catalog in relation to transitions and the validation and verification of the remote inventory. By categorizing and spatially analyzing a large number of transitions (n approximate to 18,300) and damages along the coast, particularly vulnerable transitions and hot spots of loading can be further investigated regarding the flow-structure-soil interaction. Through this, structural layouts and material combinations can be optimized for the design of sea dikes.

In the lower Florida Keys, the endangered Florida Key deer and numerous other wildlife species inhabit a vulnerable island environment susceptible to storm surges and rising seawater due to low elevation and flat terrain. Timely and reliable assessment of vegetation damage from natural disasters, such as Hurricane Irma, is crucial for effective habitat management. The study ' s overall objective is to examine Hurricane Irma ' s impact on vegetation on No Name Key, Florida, using remote sensing. The study relates the area change in vegetation obtained from remote sensing analysis to Florida Key deer population changes following the storm. The methodology involved performing a thematic change detection analysis using the following data sources: (1) aerial multispectral images (for pre- and post -Hurricane), (2) airborne lidar data (for pre- and postHurricane), (3) an existing vegetation map, and (4) soil data. A Support Vector Machine (SVM) image classification algorithm was applied to pre- and post -storm input image stacks to create pre- and post -Hurricane Irma vegetation maps. We were then able to obtain the area change information (for various vegetation categories) by performing the change detection analysis of the 2 SVM-classified images. The differences in areas following the storm were calculated for 7 affected vegetation types. Using the area change information following Hurricane Irma, we estimated the number of deer supported by the storm -affected vegetation. These estimated deer numbers, based on the area differences in post -Hurricane Irma vegetation types, were compared to observed deer numbers collected during the post -Hurricane Irma Texas A &M Natural Resources Institute (NRI) deer field survey. The results showed the following: mangroves had the largest negative area changes (area loss), followed by pinelands, hardwoods/hammocks, developed areas, and buttonwoods. Freshwater marshes had the largest positive area changes (area gain). The deer ' s preferred vegetation areas had decreased post -Hurricane Irma, resulting in a reduced deer population compared to pre -storm numbers. The predicted number of the Key deer post -Hurricane Irma fell within a 95% confidence interval of the observed deer population from the post -storm field survey. The study findings and techniques could be applied to study climate change impact, especially sea level rise. This methodology can be valuable in assessing the impact of storms on other wildlife species in similar environments. The applications and methodology are especially relevant considering the increasing frequency and intensity of storm surges and the accelerating rate of sea level rise.

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock-ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate-cryosphere-hydrology-hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze-thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

Light-absorbing organic carbon (OC), sometimes known as Brown Carbon (BrC), has been recognized as an important fraction of carbonaceous aerosols substantially affecting radiative forcing. This study firstly developed a bottom-up estimate of global primary BrC, and discussed its spatiotemporal distribution and source contributions from 1960 to 2010. The global total primary BrC emission from both natural and anthropogenic sources in 2010 was 7.26 (5.98-8.93 as an interquartile range) Tg, with 43.5% from anthropogenic sources. High primary BrC emissions were in regions such as Africa, South America, South and East Asia with natural sources (wild fires and deforestation) contributing over 70% in the former two regions, while in East Asia, anthropogenic sources, especially residential solid fuel combustion, accounted for over 80% of the regional total BrC emissions. Globally, the historical trend was mainly driven by anthropogenic sources, which increased from 1960 to 1990 and then started to decline. Res-idential emissions significantly impacted on emissions and temporal trends that varied by region. In South and Southeast Asia, the emissions increased obviously due to population growth and a slow transition from solid fuels to clean modern energies in the residential sector. It is estimated that in primary OC, the global average was about 20% BrC, but this ratio varied from 13% to 47%, depending on sector and region. In areas with high residential solid fuel combustion emissions, the ratio was generally twice the value in other areas. Uncertainties in the work are associated with the concept of BrC and measurement technologies, pointing to the need for more studies on BrC analysis and quantification in both emissions and the air. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The risk of carbon emissions from permafrost is linked to an increase in ground temperature and thus in particular to thermal insulation by vegetation, soil layers and snow cover. Ground insulation can be influenced by the presence of large herbivores browsing for food in both winter and summer. In this study, we examine the potential impact of large herbivore presence on the soil carbon storage in a thermokarst landscape in northeastern Siberia. Our aim in this pilot study is to conduct a first analysis on whether intensive large herbivore grazing may slow or even reverse permafrost thaw by affecting thermal insulation through modifying ground cover properties. As permafrost soil temperatures are important for organic matter decomposition, we hypothesize that herbivory disturbances lead to differences in ground-stored carbon. Therefore, we analyzed five sites with a total of three different herbivore grazing intensities on two landscape forms (drained thermokarst basin, Yedoma upland) in Pleistocene Park near Chersky. We measured maximum thaw depth, total organic carbon content, delta C-13 isotopes, carbon-nitrogen ratios, and sediment grain-size composition as well as ice and water content for each site. We found the thaw depth to be shallower and carbon storage to be higher in intensively grazed areas compared to extensively and non-grazed sites in the same thermokarst basin. First data show that intensive grazing leads to a more stable thermal ground regime and thus to increased carbon storage in the thermokarst deposits and active layer. However, the high carbon content found within the upper 20 cm on intensively grazed sites could also indicate higher carbon input rather than reduced decomposition, which requires further studies including investigations of the hydrology and general ground conditions existing prior to grazing introduction. We explain our findings by intensive animal trampling in winter and vegetation changes, which overcompensate summer ground warming. We conclude that grazing intensity-along with soil substrate and hydrologic conditions-might have a measurable influence on the carbon storage in permafrost soils. Hence the grazing effect should be further investigated for its potential as an actively manageable instrument to reduce net carbon emission from permafrost.

The China-Pakistan Economic Corridor (CPEC), a key hub for trade, is susceptible to glacial lake outburst floods. The distributions and types of glacial lakes in the CPEC are not well documented. In this study, cloud-free imagery acquired using the Landsat 8 Operational Land Imager during 2016-2018 was used to delineate the extent of glacial lakes in the mountainous terrain of the CPEC. In the study domain, 1341 glacial lakes (size >= 0.01 km(2)) with a total area of 109.76 +/- 9.82 km(2) were delineated through the normalized difference water index threshold method, slope analysis, and a manual rectification process. On the basis of the formation mechanisms and characteristics of glacial lakes, four major classes and eight subclasses of lakes were identified. In all, 492 blocked lakes (162 end moraine-dammed lakes, 17 lateral moraine-dammed lakes, 312 other moraine-dammed lakes, and 1 ice-blocked lake), 723 erosion lakes (123 cirque lakes and 600 other erosion lakes), 86 supraglacial lakes, and 40 other glacial lakes were identified. All lakes were distributed between 2220 and 5119 m a.s.l. At higher latitudes, the predominate lake type changed from moraine-related to erosion. From among the Gez, Taxkorgan, Hunza, Gilgit, and Indus basins, most glacial lakes were located in the Indus Basin. The number and area of glacial lakes were larger on the southern slopes of the Karakoram range.