Ground ice, cryostratigraphical and sediment analyses have been done on samples from 16 boreholes covering the different landforms in the lower part of the valley Longyeardalen, where the largest settlement in Svalbard, Longyearbyen, is located. This allows the production of the first ever top 1 m permafrost ice content map showing the spatial distribution of ground ice (excess ice content) for the Longyearbyen area based on the collected ground ice data and the quaternary geology map of the valley. The valley was infilled since deglaciation with up to 45 m of mainly alluvial sediment and marine mud, whereas colluvial and till deposits with thicknesses from less than 1 m to more than 7 m are dominating the hillsides surrounding the valley. Rock glaciers and ice cored moraines are the landforms with the highest ice content, with assumed over 20% excess ice in the top metre of permafrost. Till and solifluction material has a medium ice content with 10%-20% excess ice content, whereas colluvial deposits have a low ice content with 5%-10% excess ice content. These landforms all have an active layer thickness between 1.6 and 2.2 m. Alluvial deposits in the valley floor has the lowest ice content with 0%-2% excess ice content. Pore ice, suspended ice and reticulate cryostructures dominates the ground ice types, with layered, lenticular and porphyritic cryostructures also present. Marine sediments are widespread and only found in the lower parts of the valley beneath the marine limit. These findings are important to understand and to be prepared for increased landslide risk that is expected due climate warming thawing the top of permafrost and bringing more rainfall in the near future.

The root-knot nematode, Meloidogyne javanica, is one of the most damaging plant-parasitic nematodes, affecting chickpea and causing substantial yield losses worldwide. The damage potential and population dynamics of this nematode in chickpea in Ethiopia have yet to be investigated. In this study, six chickpea cultivars were tested using 12 ranges of initial population densities (Pi) of M. javanica second-stage juveniles (J2): 0, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64 and 128 J2 (g dry soil)-1 in a controlled glasshouse pot experiment. The Seinhorst yield loss and population dynamics models were fitted to describe population development and the effect on different measured growth variables. The tolerance limit (TTFW) for total fresh weight ranged from 0.05 to 1.22 J2 (g dry soil)-1, with corresponding yield losses ranging from 31 to 64%. The minimum yield for seed weight (mSW) ranged from 0.29 to 0.61, with estimated yield losses of 71 and 39%. The 'Haberu' and 'Geletu' cultivars were considered good hosts, with maximum population densities (M) of 16.27 and 5.64 J2 (g dry soil)-1 and maximum multiplication rate (a) values of 6.25 and 9.23, respectively. All other cultivars are moderate hosts for M. javanica; therefore, it is crucial to initiate chickpea-breeding strategies to manage the tropical root-knot nematode M. javanica in Ethiopia.

The alpine ecosystems of the Qinghai-Tibet Plateau (QTP) provide multiple ecosystem services. In recent decades, these ecosystem services have been profoundly affected by climate change, human activity, and frozen ground degradation. However, related research remains lacking to date in the QTP. To address this gap, the upper reaches of the Shule River, a typical cryospheric-dominated basin in the QTP, was selected. We simultaneously assessed the spatial-temporal patterns and driving factors of ecosystem services, including habitat quality (HQ), net primary productivity (NPP), water conservation (WC), carbon storage (CS), water yield (WY), green space recreation (GSR), and total ecosystem service (TES), by employing the InVEST, CASA, and Noah-MP land surface models in combination with remote sensing and field survey data. Our results showed that: (1) HQ, NPP, WC, CS, WY, and GSR all increased significantly from 2001 to 2020 at rates of 0.004 a(-1), 1.920 g Cm(-2)a(-1), 0.709 mma(-1), 0.237 Mg & sdot;ha(-1)a(-1), 0.212 x 10(8) m(3)a(-1), and 0.038 x 10(9) km(2)a(-1) (P < 0.05), respectively; (2) warm and humid climates, combined with shrinking of barren, contributed to the increases in HQ, NPP, WC, CS, WY, and GSR; (3) frozen ground degradation had promoting effects on HQ, NPP, CS, GSR, and TES, while inhibiting effects were observed on WY and WC (P < 0.05); (4) synergies among ecosystem services were prominent over the past 20 years; (5) the total ecosystem service value increased significantly at a rate of 1.18 x 10(9) CNYa(-1) from 2001 to 2020 (P < 0.05), primarily due to the increase in the provisioning service value.

With global warming and the intensification of human activities, frozen soils continue to melt, leading to the formation of thermokarst collapses and thermokarst lakes. The thawing of permafrost results in the microbial decomposition of large amounts of frozen organic carbon (C), releasing greenhouse gases such as carbon dioxide (CO2) and methane (CH4). However, little research has been done on the thermo-water-vapor-carbon coupling process in permafrost, and the interactions among hydrothermal transport, organic matter decomposition, and CO2 transport processes in permafrost remain unclear. We considered the decomposition and release of organic C and established a coupled thermo-water-vapor-carbon model for permafrost based on the study area located in the Beiluhe region of the Qingzang Plateau, China. The model established accurately reflected changes in permafrost temperature, moisture, and C fluxes. Dramatic changes in temperature and precipitation in the warm season led to significant soil water and heat transport, CO2 transport, and organic matter decomposition. During the cold season, however, the soil froze, which weakened organic matter decomposition and CO2 transport. The sensitivity of soil layers to changes in the external environment varied with depth. Fluctuations in energy, water, and CO2 fluxes were greater in shallow soil layers than in deeper ones. The latent heat of water-vapor and water-ice phase changes played a crucial role in regulating the temperature of frozen soil. The low content of soil organic matter in the study area resulted in a smaller influence of the decomposition heat of soil organic matter on soil temperature, compared to the high organic matter content in other soil types (such as peatlands).

The Third Pole region, encompassing the vast Himalayan and Tibetan Plateau, is undergoing rapid cryosphere and ecological transformations. This review synthesizes findings from 93 peer-reviewed studies (2000-2024) to evaluate the interactions between glacier retreat, permafrost degradation, and material cycling (carbon, methane, and nitrogen). Mean air temperature has increased by 0.3-0.4 degrees C per decade, while glaciers have lost nearly 36% of their area since the 1990s. Permafrost active layer thickness has deepened by more than 50%, releasing carbon dioxide (CO2), and methane (CH4) previously locked in frozen soils into the atmosphere and water systems. Methane fluxes from wetlands, lakes, and hydrates amplify warming feedback, while nitrogen deposition and fertilizer inputs alter ecosystem nutrient cycling and elevate nitrous oxide (N2O) risk. These processes intensify feedback loops that accelerate regional and global climate change. The findings highlight the Third Pole's role as both a critical water tower for Asia and a major contributor to global greenhouse gas budgets under warming scenarios. Effective policy responses require black carbon mitigation, GLOF early warning systems, permafrost-resilient infrastructure, sustainable nitrogen management, and regional data-sharing platforms. Future research should prioritize long-term monitoring, interdisciplinary flux measurements, and integrative modeling to better capture cryosphere-hydrosphere-biosphere-atmosphere interactions. The stability of the Third Pole is a must for global climate resilience.

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.

Permafrost degradation under climate warming plays a crucial role in hydrological and ecological processes, including the regional water cycle and terrestrial carbon balance. The Tibetan Plateau (TP), which contains the largest expanse of high-altitude permafrost globally, remains understudied in terms of how permafrost degradation affects surface water resources and regional carbon dynamics. Using permafrost simulation models and quantitative analysis, we assess the spatiotemporal impacts of permafrost degradation on surface water resources and carbon dynamics. In the inner endorheic regions of the TP, ground ice meltwater contributed 12.6% of the total lake volume increase from 2000 to 2020, accelerating lake expansion and affecting nearby infrastructure and ecosystems. Cryospheric meltwater accounted for 4.6% of total runoff in the source areas of the Yangtze, Yellow, Lancang, Yarlung Zangbo, and Nujiang Rivers in 2002-2018. This cryospheric meltwater contribution is projected to peak in the 2030s-2040s, followed by a decline, with potentially profound implications for downstream water availability. From 2000 to 2020, carbon sequestration of alpine grassland in permafrost regions is 1.05-1.29 Tg C a-1 in 2000-2020. This estimate is underestimated by approximately 35.5% to 48.1% without considering the impact of permafrost degradation. Top-down thawing of permafrost from 2002 to 2050 is projected to release 129.39 +/- 21.02 Tg C a-1 of thawed soil organic carbon (SOC), with 20.82 +/- 3.06 Tg C a-1 decomposed annually. Additionally, permafrost collapse and thermokarst lake are estimated to reduce ecosystem carbon sinks by 0.41 (0.29-0.52) Tg C a-1 in 2020. (c) 2025 The Authors. Published by Elsevier B.V. and Science China Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Understanding the mechanical behaviour of water ice-bearing lunar soil is essential for future lunar exploration and construction. This study employs discrete element method (DEM) simulations, incorporating realistic particle shapes and a flexible membrane, to investigate the effects of ice content, initial packing density, and gravitational conditions on lunar soil behaviour. Initially, we calibrated DEM model parameters by comparing triaxial tests on lunar soil without ice to physical experiments and the angle of repose simulations, validating the accuracy of our approach. Building on this, we conducted simulations on water ice-bearing lunar soil, examining stress-strain responses, shear strain, bond breakage, deviatoric fabric, and N-ring structures. DEM simulations demonstrate that increasing ice content from 0 % to 10 % elevates peak strength from 85 kPa to 240 kPa in loose samples and from 0.2 MPa to 1.62 MPa in dense samples. This strengthening aligns with microstructural stabilization evidenced by 5-ring configurations and narrowed branch vector distributions. Strain field analysis reveals greater deformation magnitudes in icy regolith, suggesting a trade-off between enhanced load-bearing capacity and reduced ductility. These quantified mechanical responses, including strength gain, structural stabilization, and strain localization, reveal the dual engineering implications of water ice in lunar soil.

The direct radiative impact of atmospheric aerosols remains more uncertain than that of greenhouse gases, largely due to the complex transformations' aerosols undergo during atmospheric aging. Sulfate aerosols have been the subject of considerable research, with a robust body of literature characterising their cooling effect. In contrast, the light-absorbing properties and warming potential of black carbon and related products remain less well understood, with limited research available to date. The present study examines the iron-catalyzed reaction of catechol in levitated microdroplets, tracked in situ using elastic light scattering spectroscopy. The reaction forms water-insoluble polycatechol aggregates, which drive a transition from homogeneous spheres to heterogeneous droplets with internal inclusions. To interpret the evolving optical behaviour, the Multiple Sphere T-Matrix (MSTM) model is employed, a method which overcomes the limitations of Mie theory by accounting for internal morphological complexity. The model provides realistic complex refractive indices and fractal parameters, though it should be noted that its solutions are not unique due to sensitivity to input assumptions and droplet variability. This underscores the necessity for supplementary measurements and more comprehensive models incorporating evaporation, chemical dynamics, and phase transitions. These findings emphasise the potential of elastic scattering spectroscopy for real-time monitoring of multiphase chemistry and offer new constraints for improving aerosol aging schemes in climate models, thereby contributing to reduced uncertainties in aerosol radiative forcing.

The MAJIS (Moons And Jupiter Imaging Spectrometer) instrument, part of the JUICE (JUpiter ICy moons Explorer) mission, is a crucial tool for investigating the composition and dynamics of Jupiter's atmosphere, and the surfaces and exospheres of its icy moons. To optimize observational planning and assess instrument performance, we have developed a radiometric simulator that accurately models MAJIS expected signal from various Jovian system targets. This simulator incorporates instrumental parameters, the spacecraft trajectory, observational constraints, and Jupiter's radiation environment. It provides essential outputs, including Signal-to-Noise Ratio (SNR) predictions and optimized instrument settings for different observational scenarios. By simulating both radiometric performance and de-spiking strategies to mitigate the impact of Jupiter radiation belt, the tool aids in refining observation strategies throughout the MAJIS operations. Several scientific applications demonstrate the simulator capabilities, from mapping the surfaces of Ganymede and Europa to detecting exospheric emissions and atmospheric composition on Jupiter. This simulator is a critical asset for maximizing MAJIS scientific return and ensuring optimal data acquisition during MAJIS exploration of the Jovian system. Study cases are presented for illustrating the capability of the simulator to model scenarios such as high-resolution mapping of Ganymede, exosphere characterization and hotspot detection on Io and Europa. These simulations confirm the potential of MAJIS for detecting key spectral features with high signal to noise ratio so as to provide major contributions to the main goals of the mission: habitability and compositional diversity in the Jovian system.