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Microbes inhabiting deep soil layers are known to be different from their counterpart in topsoil yet remain under investigation in terms of their structure, function, and how their diversity is shaped. The microbiome of deep soils (>1 m) is expected to be relatively stable and highly independent from climatic conditions. Much less is known, however, on how these microbial communities vary along climate gradients. Here, we used amplicon sequencing to investigate bacteria, archaea, and fungi along fifteen 18‐m depth profiles at 20–50‐cm intervals across contrasting aridity conditions in semi‐arid forest ecosystems of China's Loess Plateau. Our results showed that bacterial and fungal α diversity and bacterial and archaeal community similarity declined dramatically in topsoil and remained relatively stable in deep soil. Nevertheless, deep soil microbiome still showed the functional potential of N cycling, plant‐derived organic matter degradation, resource exchange, and water coordination. The deep soil microbiome had closer taxa–taxa and bacteria–fungi associations and more influence of dispersal limitation than topsoil microbiome. Geographic distance was more influential in deep soil bacteria and archaea than in topsoil. We further showed that aridity was negatively correlated with deep‐soil archaeal and fungal richness, archaeal community similarity, relative abundance of plant saprotroph, and bacteria–fungi associations, but increased the relative abundance of aerobic ammonia oxidation, manganese oxidation, and arbuscular mycorrhizal in the deep soils. Root depth, complexity, soil volumetric moisture, and clay play bridging roles in the indirect effects of aridity on microbes in deep soils. Our work indicates that, even microbial communities and nutrient cycling in deep soil are susceptible to changes in water availability, with consequences for understanding the sustainability of dryland ecosystems and the whole‐soil in response to aridification. Moreover, we propose that neglecting soil depth may underestimate the role of soil moisture in dryland ecosystems under future climate scenarios. [ABSTRACT FROM AUTHOR]

• Deep rooted vegetation significantly leads to soil water deficit with the depth and precipitation. • Relative deep soil water deficit in artificial forest decreases with precipitation increase. • Precipitation self-regulation responses to deep soil water deficit in space. • Soil water deficit is more sensitive in the sub-shallow soil layers at a depth of 1800 cm. In semi-arid and semi-humid regions, deep soil water resources are crucial for vegetation restoration to mediate the ecosystem sustainability. Yet it is still limited on understanding soil water deficit (SWD) caused by vegetation restoration and its response to soil depth and precipitation. To obtain a more accurate measurement of regional SWD, we conducted a two-year soil water monitoring to a depth of 1800 cm under typical vegetation restoration with a precipitation gradient ranging from 300 mm/y to more than 600 mm/y across the Loess Plateau of China. With increasing precipitation gradient, soil water storages in 0–1800 cm depth under both arable cropland and artificial vegetation significantly increased (p < 0.05). Compared to arable cropland, artificial vegetations (forest and shrub) had a significantly greater SWD regardless of either precipitation gradient or soil depth. In particular, the deep-rooted vegetations (R. pseudoacacia and C. korshinskii) on the Loess Plateau demonstrated the greatest SWD in the 0–1800 cm layer due to their great water consumption. Indeed, a positive relationship between the accumulated SWD and the accumulated root biomass was detected, suggesting that the increased root biomass in soil depth may promote soil water consumption. In addition, the relative SWD in artificial vegetation at a depth of 1800 cm decreased significantly (p < 0.05). These findings reveal that there is a mechanism of precipitation self-regulation on the deep SWD, of which is more sensitive in the sub-shallow soil layers. We conclude that the SWD depends on vegetation restoration, soil depth, and precipitation self-regulation, offering a reference for how afforestation affects deep soil water in the Loess Plateau region. This finding also benefits to mediate water balance processes and develop sustainable land management in water-limited regions around the world. [ABSTRACT FROM AUTHOR]

• Soil properties were the primary controls on SAS variation across the watershed. • Topography can affect SAS by influencing SOC dynamics, allocation of LUT or NDVI. • Grassland restoration was recommended for preventing soil erosion in the study area. • SEM gave new insights into the complex network of SAS drivers and the potential paths. Soil aggregate stability (SAS) is a key soil property that affects soil erosion and soil ability to support ecosystem functions. The effects of different environmental factors on SAS are extensively documented. However, the relative importance of the factors that drive variation in SAS at watershed scale is not entirely clear. To investigate the effects of the interactions of environmental variables on spatial variation in SAS, 88 sampling sites were selected across an entire watershed (1.1 km2) on the Chinese Loess Plateau (CLP), from where undisturbed soil samples were collected at the 0–10 and 10–20 cm soil depths. Three indices were used to evaluate the SAS — water-stable aggregates greater than 0.25 mm (WSA >0.25 , %), mean weight diameter (MWD, mm) and mean geometric diameter (MGD, mm). The results showed that variation of SAS across the watershed was moderate, with coefficient of variation (CV) of 23.5–38.9 %. From combined Spearman's correlation analysis (r), redundancy analysis (RDA) and structural equation modelling (SEM), it was found that soil intrinsic properties, mainly soil texture and organic carbon content (SOC), were the primary control on SAS variation. Topographic attributes, primarily wetness index (TWI) and altitude, were also important controls on SAS. These controls were either the direct or indirect effect through SOC dynamics, spatial distribution of land use (LUT) or vegetation cover (NDVI). The effect of LUT on SAS was mainly driven by SOC and TWI at the 0–10 cm depth but by NDVI and TWI at the 10–20 cm depth. SAS was positively correlated with sand content and SOC, but negatively correlated with silt content, altitude, TWI and NDVI. For LUT, SAS in the apple orchard was significantly lower than in shrubland and grassland, however, it was comparable with that in forest. Considering the effects of improving soil structure and the related economic cost, natural restoration of grassland was a good choice for preventing soil erosion in the study area. The results of this study could deepen our understanding of the controls on SAS variation and therefore become useful in soil management and vegetation restoration decisions on CLP and other regions with similar conditions. [ABSTRACT FROM AUTHOR]

• Noninvasive approach for obtaining soil water content is proposed. • Soil water content is indirectly obtained from electrical resistivity. • Prediction models for soil water content were built for different soil conditions. • The proposed approach was applied at the Chinese Loess Plateau for validation. • Models for deep soil gave better results and may be applied to other loess regions. Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R 2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT - derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world. [ABSTRACT FROM AUTHOR]