GIS-Based Analysis of the Spatial Distribution and Influencing Factors of Traditional Villages in Hebei Province, China

Traditional villages are a valuable cultural asset that occupy an important position in Chinese traditional culture. This study focuses on 206 traditional villages in Hebei Province, and aims to explore their spatial distribution characteristics and influencing factors using ArcGIS spatial analysis. The analysis shows that traditional villages in Hebei Province were distributed in clus-ters during different historical periods, and eventually formed three core clusters in Shijiazhuang, Zhangjiakou and Xingtai-Handan after different historical periods. Moreover, the overall dis-tribution of traditional villages in Hebei Province is very uneven, with clear regional differences, and most of them are concentrated in the eastern foothills of the Taihang Mountains. To identify the factors influencing traditional villages, natural environmental factors, socio-economic factors, and historical and cultural factors are considered. The study finds that socio-economic and nat-ural environmental factors alternate in the spatial distribution of traditional villages in Hebei Province. The influence of the interaction of these factors increases significantly, and so-cio-economic factors have a stronger influence on the spatial distribution. Specifically, the spatial distribution of traditional villages in Hebei Province is influenced by natural environmental fac-tors, while socio-economic factors act as drivers of spatial distribution. Historical and cultural factors act as catalysts of spatial distribution, and policy directions are external forces of spatial distribution. Overall, this study provides valuable insights into the spatial distribution charac-teristics and influencing factors of traditional villages in Hebei Province, which can be used to develop effective strategies for rural revitalisation in China

Spatial variations in soil-water carrying capacity of three typical revegetation species on the Loess Plateau, China

Re-vegetation is a necessary control measure of soil erosion in the Loess Plateau. However, excessive re-vegetation can aggravate soil water shortage, which can in turn threaten the health and services of restored ecosystems. An optimal plant cover or biomass (i.e., soil-water carrying capacity for vegetation, SWCCV) is important for regional water balance, soil protection and vegetation sustainability. The objective of this study was to determine the spatial distribution of SWCCV for three non-native tree (Robinia pseudoacaia), shrub (Caragana korshinskii) and grass (Medicago sativa) species used in the re-vegetation of the Loess Plateau. The dynamics of actual evapotranspiration (AET), net primary productivity (NPP) and leaf area index (LAI) were simulated using a modified Biome-BGC (Bio-Geochemical Cycles) model. Soil and physiological parameters required by the model were validated using field-observed AET for the three plant species at six sites in the study area. The validated model was used to simulate the dynamics of AET, NPP and LAI for the three plant species at 243 representative sites in the study area for the period 1961–2014. The results show that spatial distributions of mean AET, NPP and LAI generally increased from northwest to southeast, much the same as mean annual precipitation (MAP) gradient. In terms of maximum LAI, the ranges of optimal plant cover were 1.1–3.5 for R. pseudoacaia, 1.0–2.4 for C. korshinskii and 0.7–3.0 for M. sativa. The corresponding SWCCV, expressed as NPP were 202.4–616.5, 83.7–201.7 and 56.3–253.0 g C m−2 yr−1. MAP, mean annual temperature, soil texture and elevation were the main variables driving SWCCV under the plant species; explaining over 86% of the spatial variations in mean NPP in the study area. Further re-vegetation therefore needs careful reconsideration under the prevailing climatic, soil and topographic conditions. The results of the study provide a re-vegetation threshold to guide future re-vegetation activities and to ensure a sustainable eco-hydrological environment in the Loess Plateau

Soil moisture and electrical conductivity relationships under typical Loess Plateau land covers

Vegetation changes that are driven by soil conservation measures significantly affect subsurface water flow patterns and soil water status. Much research on water consumption and sustainability of newly introduced vegetation types at the plot scale has been done in the Loess Plateau of China (LPC), typically using local scale measurements of soil water content (SWC). However, information collected at the plot scale cannot readily be up-scaled. Geophysical methods such as electromagnetic induction (EMI) offer large spatial coverage and therefore could bridge between the scales. A non-invasive, multi-coil, frequency domain, EMI instrument was used to measure the apparent soil electrical conductivity (σ_a) from six effective depths under four typical land-covers; shrub, pasture, natural fallow and crop, in the north of the LPC. Concurrently, SWC was monitored to a depth of 4 m depth using an array of 44 neutron probes distributed along the plots. The measurements of σ_a for six effective depths and the integrated SWC over these depths, show consistent behavior. High variability of σ_a under shrub cover, in particular, is consistent with long term variability of SWC, highlighting the potential unsustainability of this land cover. Linear relationships between SWC and σ_a were established using cumulative sensitivity forward models. The conductivity-SWC model parameters show clear variation with depth, despite lack of appreciable textural variation. This is likely related to the combined effect of elevated pore water conductivity as was illustrated by the simulations obtained with water flow and solute transport models. The results of the study highlight the potential for the implementation of the EMI method for investigations of water distribution in the vadose zone of the LPC, and in particular for qualitative mapping of the vulnerability to excessive vegetation demands, and hence unsustainable land cover

Mineral N stock and nitrate accumulation in the 50 to 200 m profile on the Loess Plateau

Nitrogen (N) stored in deep profiles is important in assessing regional and/or global N stocks and nitrate leaching risk to groundwater. The Chinese Loess Plateau, which is characterized by significantly thick loess deposits, potentially stores immense stocks of mineral N, posing future threats to groundwater quality. In order to determine the vertical distributions of nitrate and ammonium content in the region, as well as to characterize the potential accumulation of nitrate in the deep loess profile, we study loess samples collected at five sites (Yangling, Changwu, Fuxian, An'sai and Shenmu) through a 50 to 200 m loess profile. The estimated storage of mineral N varied significantly among the five sites, ranging from 0.46 to 2.43 × 104 kg N ha−1. Ammonium exhibited fluctuations and dominated mineral N stocks within the whole profile at the sites, except for the upper 20–30 m at Yangling and Changwu. Measured nitrate content in the entire profile at Fuxian, An'sai and Shenmu is low, but significant accumulations were observed to 30–50 m depth at the other two sites. Analysis of δ15N and δ18O of nitrate indicates different causes for accumulated nitrate at these two sites. Mineralization and nitrification of manure and organic N respectively contribute nitrate to the 0–12 and 12–30 m profile at Changwu; while nitrification of NH4+ fertilizer, NO3− fertilizer and nitrification of organic N control the nitrate distribution in the 0–3, 3–7 and 7–10 m layer at Yangling, respectively. Furthermore, our analysis illustrates the low denitrification potential in the lower part of the vadose zone. The accumulated nitrate introduced by human activities is thus mainly distributed in the upper vadose zone (above 30 m), indicating, currently, a low nitrate leaching risk to groundwater due to a high storage capacity of the thick vadose zone in the region

Erosion reduces soil microbial diversity, network complexity and multifunctionality

While soil erosion drives land degradation, the impact of erosion on soil microbial communities and multiple soil functions remains unclear. This hinders our ability to assess the true impact of erosion on soil ecosystem services and our ability to restore eroded environments. Here we examined the effect of erosion on microbial communities at two sites with contrasting soil texture and climates. Eroded plots had lower microbial network complexity, fewer microbial taxa, and fewer associations among microbial taxa, relative to non-eroded plots. Soil erosion also shifted microbial community composition, with decreased relative abundances of dominant phyla such as Proteobacteria, Bacteroidetes, and Gemmatimonadetes. In contrast, erosion led to an increase in the relative abundances of some bacterial families involved in N cycling, such as Acetobacteraceae and Beijerinckiaceae. Changes in microbiota characteristics were strongly related with erosion-induced changes in soil multifunctionality. Together, these results demonstrate that soil erosion has a significant negative impact on soil microbial diversity and functionality

Beam test of a 180 nm CMOS Pixel Sensor for the CEPC vertex detector

The proposed Circular Electron Positron Collider (CEPC) imposes new challenges for the vertex detector in terms of pixel size and material budget. A Monolithic Active Pixel Sensor (MAPS) prototype called TaichuPix, based on a column drain readout architecture, has been developed to address the need for high spatial resolution. In order to evaluate the performance of the TaichuPix-3 chips, a beam test was carried out at DESY II TB21 in December 2022. Meanwhile, the Data Acquisition (DAQ) for a muti-plane configuration was tested during the beam test. This work presents the characterization of the TaichuPix-3 chips with two different processes, including cluster size, spatial resolution, and detection efficiency. The analysis results indicate the spatial resolution better than 5 $\mu m$ and the detection efficiency exceeds 99.5 % for both TaichuPix-3 chips with the two different processes