The presence of frozen volatiles (especially H2O ice) has been proposed in the permanently shadowed regions (PSRs) near the poles of the Moon, based on various remote measurements including the visible and near-infrared (VNIR) spectroscopy. Compared with the middle- and low-latitude areas, the VNIR spectral signals in the PSRs are noisy due to poor solar illumination. Coupled with the lunar regolith coverage and mixing effects, the available VNIR spectral characteristics for the identification of H2O ice in the PSRs are limited. Deep learning models, as emerging techniques in lunar exploration, are able to learn spectral features and patterns, and discover complex spectral patterns and nonlinear relationships from large datasets, enabling them applicable on lunar hyperspectral remote sensing data and H2O-ice identification task. Here we present H2O ice identification results by a deep learning-based model named one-dimensional convolutional autoencoder. During the model application, there are intrinsic differences between the remote sensing spectra obtained by the orbital spectrometers and the laboratory spectra acquired by state-of-the-art instruments. To address the challenges of limited training data and the difficulty of matching laboratory and remote sensing spectra, we introduce self-supervised learning method to achieve pixel-level identification and mapping of H2O ice in the lunar south polar region. Our model is applied to the level 2 reflectance data of Moon Mineralogy Mapper. The spectra of the identified H2O ice-bearing pixels were extracted to perform dual validation using spectral angle mapping and peak clustering methods, further confirming the identification of most pixels containing H2O ice. The spectral characteristics of H2O ice in the lunar south polar region related to the crystal structure, grain size, and mixing effect of H2O ice are also discussed. H2O ice in the lunar south polar region tends to exist in the form of smaller particles (similar to 70 mu m in size), while the weak/absent 2-mu m absorption indicate the existence of unusually large particles. Crystalline ice is the main phase responsible for the identified spectra of ice-bearing surface however the possibility of amorphous H2O ice beneath optically sensed depth cannot be ruled out.

Char and soot represent distinct types of elemental carbon (EC) with varying sources and physicochemical properties. However, quantitative studies in sources, atmospheric processes and light-absorbing capabilities between them remain scarce, greatly limiting the understanding of EC's climatic and environmental impacts. For in-depth analysis, concentrations, mass absorption efficiency (MAE) and stable carbon isotope were analyzed based on hourly samples collected during winter 2021 in Nanjing, China. Combining measurements, atmospheric transport model and radiative transfer model were employed to quantify the discrepancies between char-EC and soot-EC. The mass concentration ratio of char-EC to soot-EC (R-C/S) was 1.4 +/- 0.6 (mean +/- standard deviation), showing significant dependence on both source types and atmospheric processes. Case studies revealed that lower R-C/S may indicate enhanced fossil fuel contributions, and/or considerable proportions from long-range transport. Char-EC exhibited a stronger light-absorbing capability than soot-EC, as MAE(char) (7.8 +/- 6.7 m(2)g(-1)) was significantly higher than MAE(soot) (5.4 +/- 3.4 m(2)g(-1))(p < 0.001). Notably, MAE(char) was three times higher than MAE(soot) in fossil fuel emissions, while both were comparable in biomass burning emissions. Furthermore, MAE(soot) increased with aging processes, whereas MAE(char) exhibited a more complex trend due to combined effects of changes in coatings and morphology. Simulations of direct radiative forcing (DRF) for five sites indicated that neglecting the char-EC/soot-EC differentiation could cause a 10 % underestimation of EC's DRF, which further limit accurate assessments of regional air pollution and climate effects. This study underscores the necessity for separate parameterization of two types of EC for pollution mitigation and climate change evaluation.

Seasonal freezing and thawing significantly influence the migration and distribution of soil hydrothermal salts. Understanding the dynamics of hydrothermal salt forces in canal foundation soils is crucial for effective canal disease control and optimization. However, the impact on rectangular canals remains poorly understood. Therefore, field-scale studies on water-heat-salt-force-displacement monitoring were conducted for the canal. The study analyzed the changes and interaction mechanisms of water-heat-salt-force in the soil beneath the canal, along with the damage mechanisms and preventive measures. The results indicate that the most rapid changes in temperature, moisture, and salt occur in the subsoil on the canal side, with the greatest depth of freezing. Heat transfer efficiency provides an intuitive explanation for the sensitivity of ground temperature at the junction of the canal wall and subsoil to air temperature fluctuations, as well as the minimal moisture migration in this region under the subcooling effect. The temperature-moisture curve suggests that current waterheat-force and water-heat-salt-force models exhibit a delay in accurately predicting water migration within the subsoil. Rectangular canals are more susceptible to damage under peak freezing conditions, requiring a combined approach of freezing restraint and frost-heaving force to mitigate damage. These findings offer valuable insights for canal design, maintenance, and further research.

Carbonaceous aerosols play a crucial role in air pollution and radiative forcing, though their light-absorbing and isotopic characteristics remain insufficiently understood. This study analyzes optical absorption and isotopic composition in PM10 and PM2.5 particles from primary emission sources, focusing on traffic-related and solid fuel categories. We analyzed key optical properties, including the Angstrom absorption exponent (AAE), the contributions of black carbon (BC) and brown carbon (BrC) to total light absorption and the mass absorption efficiencies (MAE) of carbonaceous aerosols. AAE values were lower for traffic emission sources (0.9 to 1.3) than solid fuel emission sources (1.5 to 3), with similar values for both particle sizes. BrC contributions were more prominent at shorter wavelengths and were notably higher in solid fuel emission sources (61% to 88%) than in traffic emission sources (8% to 40%) at 405 nm. MAE values of BC at 405 nm were 2 to 20 times higher than BrC across different emissions. Particle size significantly affect MAE(BC) with PM2.5 higher when compared to PM10. Emissions from diesel concentrate mixer and raw coal burning exhibited the highest MAE(BC) for PM2.5 and PM10, respectively. Conversely, Coke had the lowest MAE(BC) but the highest MAE(BrC) for both sizes. Traffic emissions showed more stable carbon isotope ratios (delta C-13) enrichment (-29 parts per thousand to -24 parts per thousand) than solid fuels (-31 parts per thousand to -20 parts per thousand). delta C-13 of solid fuel combustion, unlike traffic sources, is found to be independent of size variation. These findings underscore the importance of source and size-specific aerosol characterization for unregulated emission sources.

Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

This paper establishes a novel full-process numerical simulation framework for analyzing the 3D seismic response of mountain tunnels induced by active faults. The framework employs a two-step approach to achieve wavefield transmission through equivalent seismic load: first, a highly efficient and accurate FMIBEM (Fast multipole indirect boundary element method) is used for large-scale 3D numerical simulations at the regional scale to generate broadband ground motions (1-5 Hz) for specific sites; subsequently, using the FEM (Finite element method), a refined simulation of the plastic deformation of surrounding rock and the elastoplastic behavior of the tunnel structure was conducted at the engineering scale. The accuracy of the framework has been validated. To further demonstrate its effectiveness, the framework is applied to analyze the impact of different fault movement mechanisms on the damage to mountain tunnels based on a scenario earthquake (Mw 6.7). By introducing tunnel structure damage classification and corresponding damage indicators, the structural damage levels of tunnels subjected to active fault movements are quantitatively evaluated. The findings demonstrate that the framework successfully simulates the entire process, from fault rupture and terrain amplification to the seismic response of tunnel structures. Furthermore, the severity of tunnel damage caused by different fault types is ranked as follows: reverse fault > normal fault > strike-slip fault.

The bank protection measures of waterways shall become more environmentally friendly in the future including the use of plants instead of stones. The low levels of protection provided by plants in the early phase after planting requires a process-based understanding of soil-wave-interaction. One process that is considered essential is liquefaction where the soil undergoes a phase-change from solid-like to fluid-like behaviour which could reduce the safety of the system. The aim of this publication is to analyse the results of column experiments on wave-induced soil liquefaction and to develop a numerical model which is able to describe the entire process from the pre-liquefaction phase to the following reconsolidation in order to support the analysis of liquefaction experiments. Numerical simulations of the column experiments were done using a fully coupled hydro-mechanical model implemented in the open-source software FEniCS. A permeability model derived from granular rheology allows the simulation of dilute as well as dense suspensions and sedimented soil skeletons. The results of the simulations show a good agreement with the experimental data. Theoretical limits in the liquefied state are captured without the common modelling segmentation into pre-and post-liquefaction phase. Due to the modular structure of the implementation, the constitutive setting can be adjusted to incorporate more complex formulations in order to study the influence of wall friction and non-linearity in soil behaviour.

Hurricane Otto caused sequential changes in tropical soil microbiota over 5 years.Acidobacteria were critical early decomposers of deposited canopy debris for 3 years.Complex C degrading fungi were critical later decomposers of debris starting at 4 years.A suite of C, N and microbial indicators should prove valuable for forest managers.Hurricanes cause significant damage to tropical forests; however, little is known of their effects on decomposition and decomposer communities. This study demonstrated that canopy debris deposited during Hurricane Otto stimulated sequential changes in soil carbon (C) and nitrogen (N) components, and decomposer microbial communities over 5 years. The initial response phase occurred within 2 years post-hurricane and appeared associated with decomposition of the labile canopy debris, suggested by: increased DNA sequences (MPS) of the Acidobacterial community (as common decomposers of labile plant material), decreases in total organic C (TOC), increased biomass C, respiration, and NH4+, conversion of organic C in biomass, and decreased MPS of complex organic C decomposing (CCDec) Fungal community. After 3 years post-hurricane, the later response phase appeared associated with decomposition of the more stable components of the canopy debris, suggested by: increased MPS of the Fungal CCDec community, TOC, stabilized Respiration, decreased Biomass C, the return to pre-hurricane levels of the conversion of organic C to biomass, and decreased MPS of Acidobacterial community. These changes in the microbial community compositions resulted in progressive decomposition of the hurricane-deposited canopy material within 5 years, resulting several potential indicators of different stages of decomposition and soil recovery post-disturbance.

Land-cover changes and new ecosystem trajectories in Interior Alaska have altered the structure and function of landscapes, with regional warming trends altering carbon and water cycling. Notably, these changes include the increased distribution of tall woody vegetation, trees and shrubs, in landscapes that historically only supported low shrub vegetation cover. In Denali National Park, Alaska, this phenomenon has altered primary succession pathways towards tundra ecosystems with the establishment and expansion of balsam poplar (Populus balsamifera) trees. In this study, we examine how snow, soil, and vegetation processes interact within this altered successional pathway towards further landscape change following glacial recession. In a sequence of outflow terraces, we found that variations in snow depth, functional soil depth, leaf area index, overstory height, and understory height were all significantly correlated with each other, with those effects largely explained by the presence of poplar. Poplar-dominated plots had deeper snowpacks, deeper functional soil depths, taller overstory and shrub heights, and greater LAI than in non-poplar plots of the same landscape age. These findings suggest a feedback cycle where the establishment of taller vegetation (here, poplar) alters ecosystem processes in the following notable ways: taller vegetation is able to trap more snow by reducing wind exposure and limiting sublimation; this snow provides water through additional snowmelt and insulation, keeping soils warmer and lessening permafrost development, leading to deeper functional soil depths. This feedback demonstrates poplar's ability to modify the environment as an ecosystem engineer, engineering a trajectory away from the otherwise expected permafrost-underlain tundra.

This study examines permafrost thermal regimes and hydrological responses to climate change in the Navarro Valley, Chile's Dry Central Andes, using a decade of ground temperature data (2013-2022) from two rock glaciers-RG1 (3805 m) and RG2 (4047 m)-alongside short-term meltwater conductivity records, meteorological data, and long-term streamflow records. We assess permafrost stability and climatic sensitivity by analyzing thermal offset data (2017-2022) and ground temperature trends. Both sites show sustained warming, but RG1 exhibits accelerated warming (+ 2.84 degrees C/decade), frequent freeze-thaw cycles, and extended thaw periods, indicating a transitional regime. In contrast, RG2 shows fewer freeze-thaw cycles and greater thermal buffering, consistent with cold permafrost. The statistical model overestimated thaw dynamics at RG2, highlighting the importance of field-based data for accurate classification. Hydrological signals at RG1-including cold, mineralized meltwater and rapid ground surface temperature stream coupling-are attributed to thawing and deeper flowpaths. Conductivity data (2014-2015) reveal solute pulses consistent with early melt events and debris interaction. Meanwhile, long-term streamflow trends indicate declining discharge. These findings suggest feedback between permafrost loss and water availability. This study underscores the divergent evolution of adjacent rock glaciers under warming by integrating thermal, hydrological, and climatic data. RG1 shows signs of degradation, while RG2 may act as a temporary refuge. Continued monitoring is essential for managing water security in vulnerable mountain regions like the Dry Andes.Graphical AbstractThis graphical abstract visually summarizes a ten-year monitoring effort of mountain permafrost and glacier hydrology in the Navarro Valley, Dry Andes (32 degrees S), with implications for water security under climate change. The left panel situates the study area within the upper Aconcagua Basin, identifying two instrumented sites within the Tres Gemelos rock glacier complex-RG1 (3805 m) and RG2 (4047 m)-and an automatic weather station. These sites were selected for continuous monitoring of ground temperature and streamflow to assess permafrost behavior in a water-stressed mountain catchment. Moving to the center, the image presents an integrated monitoring framework that links temperature-depth profiles, surface-subsurface thermal dynamics, and discharge records. Key indicators such as freeze-thaw cycle counts and thawed-day metrics are used to classify thermal regimes and detect warming trends. The upper-right panel features a conceptual model that connects permafrost degradation to hydrological responses: RG1, characterized as transitional, shows signs of enhanced shallow flow and seasonal meltwater pulses, while RG2 retains cold, thermally buffered conditions that support greater storage stability. These contrasts are further illustrated by temperature trend graphs, which reveal faster warming at RG1 (+ 2.84 degrees C/decade) compared to RG2 (+ 1.92 degrees C/decade), as well as increased thaw metrics. Below, a long-term streamflow graph (1970-2023) documents declining discharge, visually supported by a field photo of a dry riverbed. The bottom panel summarizes the study's key finding: RG1 and RG2 are evolving along divergent thermal and hydrological trajectories, underscoring the need for high-resolution monitoring to guide water resource planning in an era of warming and drought.