分类： 地球科学 >> 地理学 提交时间： 2019-03-28 合作期刊: 《干旱区科学》
摘要： Soil water is an important composition of water recycle in the soil-plant-atmosphere continuum. However, intense water exchange between soil-plant and soil-atmosphere interfaces only occurs in a certain layer of the soil profile. For deep insight into water active layer (WAL, defined as the soil layer with a coefficient of variation in soil water content >10% in a given time domain) in the Loess Plateau of China, we measured soil water content (SWC) in the 0.0–5.0 m soil profile from 86 sampling sites along an approximately 860-km long south-north transect during the period 2013–2016. Moreover, a dataset contained four climatic factors (mean annual precipitation, mean annual evaporation, annual mean temperature and mean annual dryness index) and five local factors (altitude, slope gradient, land use, clay content and soil organic carbon) of each sampling site was obtained. In this study, three WAL indices (WAL-T (the thickness of WAL), WAL-CV (the mean coefficient of variation in SWC within WAL) and WAL-SWC (the mean SWC within WAL)) were used to evaluate the characteristics of WAL. The results showed that with increasing latitude, WAL-T and WAL-CV increased firstly and then decreased. WAL-SWC showed an opposite distribution pattern along the south-north transect compared with WAL-T and WAL-CV. Average WAL-T of the transect was 2.0 m, suggesting intense soil water exchange in the 0.0–2.0 m soil layer in the study area. Soil water exchange was deeper and more intense in the middle region than in the southern and northern regions, with the values of WAL-CV and WAL-T being 27.3% and 4.3 m in the middle region, respectively. Both climatic (10.1%) and local (4.9%) factors influenced the indices of WAL, with climatic factors having a more dominant effect. Compared with multiple linear regressions, pedotransfer functions (PTFs) from artificial neural network can better estimate the WAL indices. PTFs developed by artificial neural network respectively explained 86%, 81% and 64% of the total variations in WAL-T, WAL-SWC and WAL-CV. Knowledge of WAL is crucial for understanding the regional water budget and evaluating the stable soil water reserve, regional water characteristics and eco-hydrological processes in the Loess Plateau of China.
分类： 物理学 >> 普通物理:统计和量子力学,量子信息等 提交时间： 2018-04-24 合作期刊: 《干旱区科学》
摘要： Knowledge about the effects of vegetation types on soil properties and on water dynamics in the soil profile is critical for revegetation strategies in water-scarce regions, especially the choice of vegetation type and human management measures. We focused on the analysis of the effects of vegetation type on soil hydrological properties and soil moisture variation in the 0–400 cm soil layer based on a long-term (2004―2016) experimental data in the northern Loess Plateau region, China. Soil bulk density (BD), saturated soil hydraulic conductivity (Ks), field capacity (FC) and soil organic carbon (SOC) in 2016, as well as the volumetric soil moisture content during 2004–2016, were measured in four vegetation types, i.e., shrubland (korshinsk peashrub), artificial grassland (alfalfa), fallow land and cropland (millet or potato). Compared with cropland, revegetation with peashrub and alfalfa significantly decreased BD and increased Ks, FC, and SOC in the 0–40 cm soil layer, and fallow land significantly increased FC and SOC in the 0–10 cm soil layer. Soil water storage (SWS) significantly declined in shrubland and grassland in the 40–400 cm soil layer, causing severe soil drought in the deep soil layers. The study suggested that converting cropland to grassland (alfalfa) and shrubland (peashrub) improved soil-hydrological properties, but worsened water conditions in the deep soil profile. However, natural restoration did not intensify deep-soil drying. The results imply that natural restoration could be better than revegetation with peashrub and alfalfa in terms of good soil hydrological processes in the semi-arid Loess Plateau region.
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