黄土丘陵区影响土壤水分的非气候因子之定量标征

土壤前期含水量是影响土壤侵蚀模拟与预测的重要因素之一,许多研究关注了气候(降雨等)条件对土壤前期含水量的影响。选择黄土丘陵区桑塔小流域,通过野外定点观测,分析了非气候因子(土地利用、坡度、坡向、坡位)对土壤水分的影响,提出了各因子对土壤水分的影响系数和计算方法,建立了研究区5—10月各月土壤水分模拟方程。主要结果:(1)研究区土壤水分平均含量在5—10月期间均表现为先下降后升高的趋势,在8月份达到最低。不同土地利用类型土壤水分含量由大到小的顺序依次为:梯田>坡耕地>荒草地>果园>刺槐林>柠条林。(2)依据坡度、坡向、坡位、土地利用类型对土壤水分的影响,采用标准化处理,计算出了影响土壤水分的土地利用系数、坡向系数和相对坡位系数。(3)以土壤含水量为因变量,以坡度和经过标准化处理得到的影响土壤水分的土地利用系数、坡向系数、相对坡位系数为自变量,进行回归分析,得到5—10月各月的土壤水分模拟方程,其决定系数R2为0.54～0.64,F统计量P值均远远小于0.05显著水平,可用于同类区域的土壤水分模拟。

Quantitative Marking of Non-climatic Factors Influencing Soil Moisture in Loess Hilly Area

Soil moisture is one of the most important factors influencing soil erosion modeling and prediction.A lot of research results have contributed to evaluation of climatic factors such as rainfall.The impacts of non-climatic factors,such as land-use,slope degree,slope aspects and slope position,on soil moisture were analyzed based on the field observation data in Loess hilly region.The influence coefficient on soil water of each factor and calculation method were proposed.And soil moisture simulation equation of each month from May to October was developed.The main results were:(1) The average soil water content of all land use types from May to October followed the same trend as a reversed parabola: decreasing firstly and increasing late,and the lowest point was in August from high to low level,the soil water contents were in the sequence of terraced land>cropping slope land>waste grassland>orchard land>Robinia pseudoacacia land>Caragana korshivskii land;(2) Based on the effects of slope degree,land use,slope aspect and slope position on vertical distribution of soil water contents,the influence coefficient on soil water of each factor was calculated by standardization algorithm;(3) Taking monthly soil water contents as the dependent variable and slope degree,standardized slope aspect and land-use type,relative slope position coefficient as the independent variables,the regression equations for each month from May to October were fitted.The regression result showed that the R2 ranged from 0.54 and 0.64 and P valuables are far less than 0.05.And it was meaningful for soil water simulation in the similar areas.