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Hydrology and Earth System Sciences

机译:水文与地球系统科学

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Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture. Nevertheless, its spatio-temporal patterns in agriculturally used landscapes that are affected by multiple natural (rainfall, soil, topography etc.) and agronomic (fertilisation, soil management etc.) factors are often not well known. The aim of this study is to determine the dominant factors governing the spatio-temporal patterns of surface soil moisture in a grassland and an arable test site that are located within the Rur catchment in Western Germany. Surface soil moisture (0–6 cm) was measured in an approx. 50×50 m grid during 14 and 17 measurement campaigns (May 2007 to November 2008) in both test sites. To analyse the spatio-temporal patterns of surface soil moisture, an Empirical Orthogonal Function (EOF) analysis was applied and the results were correlated with parameters derived from topography, soil, vegetation and land management to link the patterns to related factors and processes. For the grassland test site, the analysis resulted in one significant spatial structure (first EOF), which explained 57.5% of the spatial variability connected to soil properties and topography. The statistical weight of the first spatial EOF is stronger on wet days. The highest temporal variability can be found in locations with a high percentage of soil organic carbon (SOC). For the arable test site, the analysis resulted in two significant spatial structures, the first EOF, which explained 38.4% of the spatial variability, and showed a highly significant correlation to soil properties, namely soil texture and soil stone content. The second EOF, which explained 28.3% of the spatial variability, is linked to differences in land management. The soil moisture in the arable test site varied more strongly during dry and wet periods at locations with low porosity. The method applied is capable of identifying the dominant parameters controlling spatio-temporal patterns of surface soil moisture without being affected by single random processes, even in intensively managed agricultural areas.
机译:土壤水分是水文学,气象学和农业中的基本变量之一。然而,在受多种自然因素(降雨,土壤,地形等)和农艺因素(施肥,土壤管理等)影响的农业使用景观中,其时空分布通常并不为人所知。这项研究的目的是确定控制位于德国西部Rur流域内的草地和可耕地的表层土壤水分时空分布的主导因素。表土湿度(0-6厘米)的测量约为在两个测试地点的14和17个测量活动(2007年5月至2008年11月)中使用50×50 m网格。为了分析地表土壤水分的时空格局,应用了经验正交函数分析(EOF),并将结果与​​地形,土壤,植被和土地管理等相关参数相关联,以将格局与相关因素和过程联系起来。对于草地测试点,分析得出一个重要的空间结构(第一个EOF),解释了与土壤特性和地形有关的57.5%的空间变异性。在雨天,第一个空间EOF的统计权重更大。在土壤有机碳(SOC)含量较高的地区,可以找到最高的时间变异性。对于耕地测试点,分析得出两个重要的空间结构,第一个EOF解释了38.4%的空间变异性,并显示出与土壤特性(即土壤质地和土壤石块含量)的高度相关性。第二个EOF解释了28.3%的空间变异性,与土地管理的差异有关。在低孔隙率的干旱和潮湿时期,可耕试验地点的土壤水分变化更大。所应用的方法能够确定控制表层土壤水分时空分布的主要参数,而不受单个随机过程的影响,即使在集约化管理的农业地区也是如此。

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