An anomalous warm weather event in the Antarctic McMurdo Dry Valleys on 18 March 2022 created an opportunity to characterize soil biota communities most sensitive to freeze-thaw stress. This event caused unseasonal melt within Taylor Valley, activating stream water and microbial mats around Canada Stream. Liquid water availability in this polar desert is a driver of soil biota distribution and activity. Because climate change impacts hydrological regimes, we aimed to determine the effect on soil communities. We sampled soils identified from this event that experienced thaw, nearby hyper-arid areas, and wetted areas that did not experience thaw to compare soil bacterial and invertebrate communities. Areas that exhibited evidence of freeze-thaw supported the highest live and dead nematode counts and were composed of soil taxa from hyper-arid landscapes and wetted areas. They received water inputs from snowpacks, hyporheic water, or glacial melt, contributing to community differences associated with organic matter and salinity gradients. Inundated soils had higher organic matter and lower conductivity (p < .02) and hosted the most diverse microbial and invertebrate communities on average. Our findings suggest that as liquid water becomes more available under predicted climate change, soil communities adapted to the hyper-arid landscape will shift toward diverse, wetted soil communities.

Hydraulic conductivity plays a significant role in the evolution of liquefaction phenomena induced by seismic loading, influencing the pore water pressure buildup and dissipation, as well as the associated settlement during and after liquefaction. Experimental evidence indicates that hydraulic conductivity varies significantly during and after seismic excitation. However, most previous studies have focused on experimentally capturing soil hydraulic conductivity variations during the post-shaking phase, primarily based on the results at the stage of excess pore water pressure dissipation and consolidation of sand particles after liquefaction. This paper aims to quantify the variation of hydraulic conductivity during liquefaction, covering both the co-seismic and postshaking phases. Adopting a fully coupled solid-fluid formulation (u-p), a new back-analysis methodology is introduced which allows the direct estimation of the hydraulic conductivity of a soil deposit during liquefaction based on centrifuge data or field measurements. Data from eight well-documented free-field dynamic centrifuge tests are then analysed, revealing key characteristics of the variation of hydraulic conductivity during liquefaction. The results show that hydraulic conductivity increases rapidly at the onset of seismic shaking but gradually decreases despite high pore pressures persisting. The depicted trends are explained using the KozenyCarman equation, which highlights the combined effects of seismic shaking-induced agitation, liquefaction, and solidification on soil hydraulic conductivity during the co-seismic and post-shaking phases.

Stability analysis of undrained anisotropic slopes has been made by a semi-analytical approach. The variational calculus is employed to find the critical sliding surface and the extremized resistance functional for general undrained anisotropic materials in seismic condition. A complete set of formulation with the solution is presented. In this approach, no failure surface is assumed and it is obtained throughout the solution procedure. The semi-analytical solution highly increases the efficiency of the computational effort and the accuracy of the results. The stability factor is reported for a series of anisotropic slopes in undrained condition subject to a range of pseudo-static earthquake accelerations. Finally, the results are interpreted and discussed by AI to make a comparison between a manual and an AI-assisted interpretations.

Accurate structural health monitoring (SHM) is crucial for ensuring safety and preventing catastrophic failures. However, conventional parameter identification methods often assume a fixed-base foundation, neglecting the significant influence of soil-structure interaction (SSI) on the dynamic response, leading to inaccurate damage assessments, especially under seismic loading. Therefore, we introduce a novel approach that explicitly incorporates SSI effects into parameter identification for frame structures, utilizing an optimized variational mode decomposition (VMD) technique. The core innovation is the application of the Subtraction Average-Based Optimizer (SABO) algorithm, coupled with permutation entropy as the fitness function, to optimize the critical VMD parameters. This SABO-VMD method was rigorously validated through a shaking table test on a 12-story frame structure on soft soil. Comparative analysis with EMD and conventional VMD demonstrated that SABO-VMD provides a superior time-frequency representation of the structural response, capturing non-stationary characteristics more effectively. A novel energy entropy index, derived from the SABO-VMD output with SSI, was developed for quantitative damage assessment. It revealed 8.1% lower degree of structural damage compared to the fixed-base assumption. The proposed SABO-VMD-based approach, by explicitly accounting for SSI, offers a substantial advancement in SHM of frame structures, leading to more reliable safety evaluations and improved seismic resilience.

Forest soil is crucial in climate change mitigation, food security, and biogeochemical nutrient cycling. Mixed Sal forests enhance soil organic matter, improve nutrient availability, and regulate pH dynamics. However, anthropogenic disturbances, including deforestation and land-use changes, significantly alter forest cover, leading to shifts in soil physicochemical and microbial properties. These impacts necessitate rigorous monitoring and comprehensive assessment. Therefore, we investigated the effects of contrasting conditions- closed (no human activities) and open (human interferences) mixed Sal Forest on the vertical and seasonal dynamics of microbial biomass carbon (SMBC). Results revealed that the closed mixed Sal Forest had significantly higher SMBC than the open mixed Sal Forest across the soil profile (D1-D5) with a strong seasonal effect. Closed mixed Sal Forest had 60% higher SMBC in D1 than open mixed Sal Forest while it reduced with depth and 17.1 to 56.7% higher SMBC in the subsurface to bottom-most soil profile (D2-D5). Moreover, SMBC was higher in the monsoon period in both forests. The SMBC reduced by 24.2 to 45.1% in the post-monsoon period while reduction was more intense in the pre-monsoon period (48.1 to 68.2%) compared to the monsoon period under closed mixed Sal Forest. Similarly, the decline was more intense in the open mixed Sal Forest, where SMBC declined 12.1 to 54% in the post-monsoon period and 56.1 to 76.2% in the pre-monsoon period compared to the monsoon period. The study indicates that human interference in mixed Sal forests leads to loss of forest cover, negatively affecting microbiological properties and reducing soil fertility, which weakens the forest's resilience to climate change. Additionally, SMBC exhibits seasonal variations, reflecting responses to environmental conditions. These results underline the need to reduce human disturbances and enhance forest conservation strategies to ensure soil sustainability and ecosystem stability.

Glaciers playa vital role in providing water resources for drinking, agriculture, and hydro-electricity in many mountainous regions. As global warming progresses, accurately reconstructing long-term glacier mass changes and comprehending their intricate dynamic relationships with environmental variables are imperative for sustaining livelihoods in these regions. This paper presents the use of eXplainable Machine Learning (XML) models with GRACE and GRACE-FO data to reconstruct long-term monthly glacier mass changes in the Upper Yukon Watershed (UYW), Canada. We utilized the H2O-AutoML regression tools to identify the best performing Machine Learning (ML) model for filling missing data and predicting glacier mass changes from hydroclimatic data. The most accurate predictive model in this study, the Gradient Boosting Machine, coupled with explanatory methods based on SHapley Additive eXplanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) analyses, led to automated XML models. The XML unveiled and ranked key predictors of glacier mass changes in the UYW, indicating a decrease since 2014. Analysis showed decreases in snow water equivalent, soil moisture storage, and albedo, along with increases in rainfall flux and air temperature were the main drivers of glacier mass loss. A probabilistic analysis hinging on these drivers suggested that the influence of the key hydrological features is more critical than the key meteorological features. Examination of climatic oscillations showed that high positive anomalies in sea surface temperature are correlated with rapid depletion in glacier mass and soil moisture, as identified by XML. Integrating H2OAutoML with SHAP and LIME not only achieved high prediction accuracy but also enhanced the explainability of the underlying hydroclimatic processes of glacier mass change reconstruction from GRACE and GRACE-FO data in the UYW. This automated XML framework is applicable globally, contingent upon sufficient high-quality data for model training and validation.

A sustainable use of croplands should utilize beneficial services provided by their resident soil microbiome. To identify potentially adverse environmental effects on soil microbiomes in the future, a better understanding of their natural variability is fundamental. Here, we characterized the abundance and diversity of soil microbial communities over 2 years at two-week intervals on three neighboring fields at an operational farm in Northern Germany. Field soils differed in texture (clay, loam) and tillage (soil conservation vs. conventional). PCRamplicon analyses of soil DNA revealed distinct temporal variations of bacteria, archaea, fungi, and protists (Cercozoa and Endomyxa). Annual differences and seasonal effects on all microbial groups were detected. In addition to soil pH, prokaryotic communities varied with total soil C and N, but fungi with temperature and precipitation. The C/N ratio had contrasting effects on prokaryotic phyla and protistan classes, but all fungal phyla responded positively. Irrespective of the sampling date, prokaryotic and fungal but not protistan community compositions from the three soils were distinct. Compositional turnover rates were higher for fungi and protists than for prokaryotes and, for all, lower in clay. Conventional tillage had the strongest effect on protist diversity. In co-occurrence networks, most nodes were provided by prokaryotes, but highly connected nodes by predatory protists in the first, and by saprotrophic fungi in the second year. The temporal variation established here can provide insights of what is natural and thus below the limits of concern in detecting adverse effects on the soil microbiome.

This research investigated the impact of various mixed sowing combinations on soil nutrients and grass yield within the rhizosphere across different seasons. Three varieties of leguminous forages-Medicago sativa 'Gannong No. 3' (GN3), M. sativa 'Gannong No. 9' (GN9), and M. sativa 'Juneng No. 7' (JN7)-as well as three varieties of grasses-Leymus chinensis 'Longmu No. 1' (LC), Agropyron mongolicum 'Mengnong No. 1' (AC), and Bromus inermis 'Yuanye' (BI)-were used as experimental materials for mixed sowing combinations; the monocultures of each material served as controls. We explored the seasonal effects of different legumes and grasses intercropping combinations on rhizosphere soil nutrients and grass yield in the Hexi Corridor region of China. The results indicated that the levels of soil enzyme activity, microbial biomass, and soil nutrients in the rhizosphere across the various treatments followed the following sequence: summer > spring > autumn. The soil enzyme activities and microbial biomass of various mixed sowing combinations were significantly higher than those of the monocultures within the same growing season (p < 0.05). Specifically, the activities of alkaline phosphatase (APA), catalase (CAT), soil microbial biomass carbon (SMBC), soil microbial biomass nitrogen (SMBN), soil microbial biomass phosphorus (SMBP), soil organic matter (SOM), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) within the GN9+BI group were the highest among all treatments. The hay yields of GN3, GN9, and JN7 were markedly greater than those of their respective mixed sowing combinations (p < 0.05). Correlation analysis revealed a positive relationship between enzyme activities, microbial biomass, and soil nutrient levels. This comprehensive evaluation indicated that the mixed sowing combinations of GN9 + BI and GN9 + LC are particularly well suited for widespread adoption in the Hexi Oasis irrigation area.

Accurately determining the freeze/thaw state (FT) is crucial for understanding land-atmosphere interactions, with significant implications for climate change, ecological systems, agriculture, and water resource management. This article introduces a novel approach to assess FT dynamics by comparing the new diurnal amplitude variations (DAV) algorithm with the traditional seasonal threshold algorithm (STA) based on the soil moisture active passive (SMAP) brightness temperature data. Utilizing soil temperature profiles from 44 sites recorded by the National Ecological Observatory Network between July 2019 and June 2022. The results reveal that the DAV algorithm demonstrates a remarkable potential for capturing FT signals, achieving an average accuracy of 0.82 (0.89 for the SMAP-FT product) across all sites and a median accuracy of 0.94 (0.92 for the SMAP-FT product) referring to soil temperature at 0.02 m. Notably, the DAV algorithm outperforms the SMAP-adopted STA in 25 out of 44 sites. The accuracy of the DAV algorithm is affected by daily temperature fluctuations and geographical latitudes, while the STA exhibits limitations in certain regions, particularly those with complex terrains or variable climatic patterns. This article's innovative contribution lies in systematically comparing the performance of the DAV and STA algorithms, providing valuable insights into their respective strengths and weaknesses.

The presence of cracks significantly impacts the hydrological behaviour of clay embankments. This study aimed to enhance understanding of the complex interplay between the amount and propagation of desiccation cracks and seasonal variations. A full-scale embankment was constructed and equipped with an array of instruments, including pore water pressure, volumetric water content (VWC), and crack observer. The results suggested that continues cracks at shallow depths (0.5 m) exhibit significant seasonal fluctuations due to pronounced soil-atmosphere interactions, facilitating rapid water movement and substantial changes in crack width. In contrast, discontinuous cracks at intermediate depths (0.5 m) are less affected by seasonal changes, but they can propagate and connect over time due to repeated wetting and drying cycles. The crack intensity factor (CIF) above 0.4 m is highly sensitive to climatic variations, leading to pronounced fluctuations with changes in rainfall and dry conditions. The twofold increase in CIF values leads to a significant reduction in VWC (by 13.5%) at the depth of 0.25 m under the same atmospheric water balance. However, this effect is less pronounced at greater depths, such as 0.5 m, as discontinuous cracks are less effective in facilitating rapid drainage and moisture loss.