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.

Landslides, a prevalent natural disaster, wreak havoc on both human lives and vital infrastructure, making them a significant global concern. Their devastating impact is immeasurable, necessitating proactive measures to minimize their occurrence. The ability to accurately forecast the severity of a landslide, including its potential fatality rate and the scale of destruction it may cause, holds tremendous potential for prevention and mitigation to reduce the risk and the damage caused by a landslide to infrastructure and life. In this study, the spatial variability in severity of landslides (in terms of mortality rates) and its dependence on various meteorological, geographical and soil composition has been attempted to be established. To do this, Ordinary Least Squares (global) and various Geographically Weighted (local) models have been employed to observe the varying relation between mortality rates and its various causative factors. Existence of geographical heterogeneity in the relationships is also investigated. The spatial pattern of landslide mortality and its associations with various causative variables in the South Asian Region are investigated and analysed. Through this, insights into targeting of prevention and mitigation measures for landslides based on a given location can be obtained by studying the various forms of heterogeneous spatial associations observed. The outcomes highlight that the local models in the form of Gaussian GWR and Poisson GWR outperform their global counterparts by a huge margin with better R2 and Adj R2 values. In comparison with Poisson GWR and Gaussian GWR, it is seen that Poisson GWR outperforms Gaussian GWR in terms of Mean Absolute Error, Mean Squared Error and Corrected Akaike Information Criterion. Furthermore, several intriguing local relationships patterns are also noted.

Sudden and unforeseen seismic failures of coal mine overburden (OB) dump slopes interrupt mining operations, cause loss of lives and delay the production of coal. Consideration of the spatial heterogeneity of OB dump materials is imperative for an adequate evaluation of the seismic stability of OB dump slopes. In this study, pseudo-static seismic stability analyses are carried out for an OB dump slope by considering the material parameters obtained from an in-situ field investigation. Spatial heterogeneity is simulated through use of the random finite element method (RFEM) and the random limit equilibrium method (RLEM) and a comparative study is presented. Combinations of horizontal and vertical spatial correlation lengths were considered for simulating isotropic and anisotropic random fields within the OB dump slope. Seismic performances of the slope have been reported through the probability of failure and reliability index. It was observed that the RLEM approach overestimates failure probability (Pf) by considering seismic stability with spatial heterogeneity. The Pf was observed to increase with an increase in the coefficient of variation of friction angle of the dump materials. Further, it was inferred that the RLEM approach may not be adequately applicable for assessing the seismic stability of an OB dump slope for a horizontal seismic coefficient that is more than or equal to 0.1.

As major components of terrestrial ecosystems, forest ecosystems play an important role in sequestering carbon and hence mitigating climate change. Canopy height is a crucial factor characterizing the structure and function of forest ecosystems, yet the driving mechanism of forest canopy height receives less attentions in China. Here, we utilize the satellite-based forest canopy height product with several environmental and climate factors (e.g. forest age, temperature, etc.) to delineate the spatial distributions of forest canopy height and its drivers in China at 1 km spatial resolution during the period of 2014 to 2018. The random forest is employed for identifying the dominant factors at province level, while Shapley additive explanations (SHAP) analysis is further incorporated at pixel-level to dig into the specific contributions of each driver. The results show that forest age primarily dominates the spatial distributions of forest canopy height across different forest ecosystems of China, followed by mean annual precipitation, soil type, and aspect. SHAP analysis further indicates that other factors, such as soil moisture and wind speed, also play critical roles to shape the spatial patterns of forest canopy height in China, which could not be revealed from province-level random forest analyses. Such results emphasize the priority of incorporating SHAP analysis with random forest to advance our understanding of forest canopy height distributions and benefit future projections. Our study highlights the necessity to characterize the spatial heterogeneity of forest canopy height, which is critical for accurate estimations of forest and even terrestrial carbon sink in China, facilitating the achievement of the goal of carbon peak in 2030 and carbon neutrality in 2060.

Earth's cryosphere and biosphere are extremely sensitive to climate changes, and transitions in states could alter the carbon emission rate to the atmosphere. However, little is known about the climate sensitivities of frozen soil and vegetation production. Moreover, how does climate heterogeneity control the spatial patterns of such sensitivities, and influence regional vulnerability of both frozen soil and vegetation production? Such questions are critical to be answered. We compiled long-time-series dataset including frozen soil depth (FD), normalized difference vegetation index (NDVI), and temperature and precipitation across Tibetan Plateau to quantify their sensitivities. Results reveal large spatial heterogeneity in FD and NDVI sensitivities. Precipitation alleviated FD sensitivities to warming in the cold northeast zone but accelerated FD sensitivities to precipitation in the warm south and southeast. Meanwhile, the positive warming effect on the NDVI was largely offset by slow increase of precipitation. Areas with high FD decreasing rate were found in northeast, inland, and south and southeast zones. Predominate area across the nine eco-regions are characterized as medium FD decreasing rate, and are synchronized with positive NDVI response in inland and west Himalayas, but negative in northeast and south and southeast. Precipitation restriction on NDVI would be pronounced in moist south and southeast. Our study provides new information that makes a much-needed contribution to advancing our understandings of the effects of global climate change on cryosphere and biosphere, which has important implications for global climate and our ability to predict, and therefore prepare for, future global climatic changes. Our attempt confirms that the method we used could be used to identify climate sensitivity of permafrost based on substantial observation data on active layer dynamics in future.

Mountain ecosystems are experiencing rapid warming resulting in ecological changes worldwide. Projecting the response of these ecosystems to climate change is thus crucial, but also uncertain due to complex interactions between topography, climate, and vegetation. Here, we performed numerical simulations in a real and a synthetic spatial domain covering a range of contrasting climatic conditions and vegetation characteristics representative of the European Alps. Simulations were run with the mechanistic ecohydrological model Tethys-Chloris to quantify the drivers of ecosystem functioning and to explore the vulnerability of Alpine ecosystems to climate change. We correlated the spatial distribution of ecohydrological responses with that of meteorological and topographic attributes and computed spatially explicit sensitivities of net primary productivity, transpiration, and snow cover to air temperature, radiation, and water availability. We also quantified how the variance in several ecohydrological processes, such as transpiration, quickly diminishes with increasing spatial aggregation, which highlights the importance of fine spatial resolution for resolving patterns in complex topographies. We conducted controlled numerical experiments in the synthetic domain to disentangle the effect of catchment orientation on ecohydrological variables, such as streamflow. Our results support previous studies reporting an altitude threshold below which Alpine ecosystems are water-limited in the drier inner-Alpine valleys and confirm that the wetter areas are temperature-limited. High-resolution simulations of mountainous areas can improve our understanding of ecosystem functioning across spatial scales. They can also locate the areas that are the most vulnerable to climate change and guide future measurement campaigns.

Soil properties such as soil organic carbon (SOC) stocks and active-layer thickness are used in earth system models (ESMs) to predict anthropogenic and climatic impacts on soil carbon dynamics, future changes in atmospheric greenhouse gas concentrations, and associated climate changes in the permafrost regions. Accurate representation of spatial and vertical distribution of these soil properties in ESMs is a prerequisite for reducing existing uncertainty in predicting carbon-climate feedbacks. We compared the spatial representation of SOC stocks and active-layer thicknesses predicted by the coupled Model Intercomparison Project Phase 5 (CMIP5) ESMs with those predicted from geospatial predictions, based on observation data for the state of Alaska, USA. For the geospatial modeling, we used soil profile observations (585 for SOC stocks and 153 for active-layer thickness) and environmental variables (climate, topography, land cover, and surficial geology types) and generated fine-resolution (50-m spatial resolution) predictions of SOC stocks (to 1-m depth) and active-layer thickness across Alaska. We found large inter-quartile range (2.5-5.5 m) in predicted active-layer thickness of CMIP5 modeled results and small inter-quartile range (11.5-22 kg m(-2)) in predicted SOC stocks. The spatial coefficient of variability of active-layer thickness and SOC stocks were lower in CMIP5 predictions compared to our geospatial estimates when gridded at similar spatial resolutions (24.7 compared to 30% and 29 compared to 38%, respectively). However, prediction errors, when calculated for independent validation sites, were several times larger in ESM predictions compared to geospatial predictions. Primary factors leading to observed differences were (1) lack of spatial heterogeneity in ESM predictions, (2) differences in assumptions concerning environmental controls, and (3) the absence of pedogenic processes in ESM model structures. Our results suggest that efforts to incorporate these factors in ESMs should reduce current uncertainties associated with ESM predictions of carbon-climate feedbacks. (C) 2016 The Authors. Published by Elsevier B.V.