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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]

Bulk density (BD) is one of the most important soil physical properties in the hydrological processes of Earth's Critical Zone (CZ). The heterogeneity and related controlling factors of surface BD have received extensive research attention. However, it is unclear whether the effects of controlling factors on BD are homogeneous or vary spatially. This study quantified topsoil BD with a high‐density sampling strategy (10 m × 10 m; n = 1134) across the ShuangChaGou watershed in the critical zone of the Chinese Loess Plateau (CLP‐CZ). Stepwise multiple linear regression (SMLR), regression kriging (RK), random forest (RF) and geographically weighted regression (GWR) were established as modelling approaches to (i) compare the performance of these four models in explaining BD variation, (ii) assess the main factors driving BD, and (iii) evaluate the spatial non‐stationary relationship between BD and its explanatory variables. GWR explained the BD variation to the same extent as the RK model (35%), outperforming both the SMLR (15%) and RF models (19%). The variation in topsoil BD depended mainly on: soil organic carbon (SOC) content, normalised difference vegetation index (NDVI), plan curvature, and slope length. The spatial distribution of the local R2, regression coefficient magnitude, significance and sign calculated by GWR revealed that the effects of variables accounting for variation in BD varied spatially. The explanatory power of these variables in terms of BD variation was strongest in northern and northeastern areas of the watershed and weakest in southern areas of the watershed. Both significant negative and positive correlations between BD and silt content, NDVI, and plan curvature were found. This study enriched traditional research linked to the complicated relationships between BD and controlling factors by incorporating spatial non‐stationarity, which is useful for improving the accuracy of hydrological modelling in the CLP‐CZ and in similar CZs worldwide. Highlights: Non‐stationary relationships between BD (N = 1134) and its explanatory variables were captured at the watershed scale.GWR model performed on par with the RK and better than the SMLR or RF model.Silt content, NDVI, and plan curvature showed both significant negative and positive effects on BD.Spatial non‐stationarity of BD should be considered for improving the performance of hydrological modeling. [ABSTRACT FROM AUTHOR]

This paper introduces a scheme to compensate IR drop in real time based on the real ELVDD in display. The real ELVDD of each row of pixels at the time of charge and discharge is obtained by designing detection circuits on both sides of the display, and the pixel voltage Vdata is adjusted by the detection voltage. In this way, the display problem caused by local and global IR drop can be solved in a direct and effective way. [ABSTRACT FROM AUTHOR]