不同地下水矿化度条件下柽柳土柱的水盐分布特征

通过探讨土壤水盐分布特征对地下水矿化度的响应规律,为泥质海岸带盐碱土的合理利用和水土保持防护林的栽植管理提供科学依据.以栽植3年生柽柳苗木的土壤柱体为研究对象,在1.8m的地下水水位下,分别模拟设置淡水(0 g/L)、微咸水(3 g/L)、咸水(8 g/L)和盐水(20 g/L)4种地下水矿化度,并以不栽植柽柳的土壤柱体为对照,测定分析不同地下水矿化度处理的土壤水分、盐分以及溶液绝对浓度等水盐参数.结果表明:地下水矿化度可显著影响不同剖面的土壤水盐参数.随地下水矿化度的升高,整个土柱的含水量和含盐量均呈现升高趋势,而土壤溶液绝对浓度呈下降趋势.其中:柽柳土柱相对含水量均值在4种矿化度下,分别比对照下降24.4％、20.6％、11.3％和4.7％;微咸水、咸水和盐水矿化度下,柽柳土柱的含盐量均值,分别比对照下降6.7％、5.6％和8.7％.不同地下水矿化度条件下,随土壤深度的增加,土壤水分呈升高趋势,土壤溶液绝对浓度呈下降趋势,土壤盐分则表现为先下降再升高,并在80 cm土壤深度下,盐分达最低值.地下水矿化度的升高,有利于盐分随水分向土壤表层迁移.栽植柽柳可显著降低土柱的含水量、含盐量和土壤溶液绝对浓度,但随地下水矿化度的升高,栽植柽柳对土壤水分的降低作用在减弱,而抑制盐分作用在增强.     

The spatial variability of soil nitrates in arable and pasture landscapes: implications for the development of geographical information system models of nitrate leaching

Abstract. In response to the European Community Nitrate Directive (91/676) a catchment scale Geographical Information System (GIS) model of nitrate leaching has been developed to map nitrate vulnerability and predict average weekly fluxes of nitrate from agricultural land units to surface water. This paper presents a pilot study which investigated the spatial variability of soil nitrates in order to: (1) define an appropriate pixel size for modelling N leaching; (2) quantify the within-unit variability of soil nitrate concentrations for pasture and arable fields; and (3) assist in the design of an efficient sampling strategy for estimating mean nitrate concentrations. Soil samples, taken from two 800 m transects in early September 1994, were analysed for water soluble nitrate. The arable soils had a mean nitrate-nitrogen concentration of 0.693 μg/g (S.E. 0.054 μg/g) and the pasture soils had a higher mean nitrate-nitrogen concentration of 0.86 μg/g (S.E. 0.085 μg/g). Spatial variability was investigated using variograms. The pasture data had a weak spatial relationship, whereas the arable data exhibited a strong spatial relationship which fitted a spherical variogram model (r² 0.87), with a range of 40 m. A pixel size of 40 m is suggested for nitrate modelling within the GIS based on the arable variogram and an improved sampling strategy for model validation is suggested, involving bulking sub-samples over a 40 m grid for estimating mean nitrate concentrations in combined land use and soil units.