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Estimating Soil Hydraulic Conductivity at the Field Scale With a State-Space Approach
Soil Science ( IF 1.692 ) Pub Date : 2019-06-01 , DOI: 10.1097/ss.0000000000000253
Xi Zhang , Ole Wendroth , Christopher Matocha , Junfeng Zhu

ABSTRACT A precise description of saturated (Ks) and near-saturated hydraulic conductivity (K−10) and their spatial variability is important for understanding water/solute transport in the vadose zone. However, it is laborious to measure K directly. Alternatively, K could be predicted from easily measurable soil properties using pedotransfer functions (PTFs). Because PTFs ignore the spatial relationships and covariance between soil variables, they often perform unsatisfactorily when field-scale estimations of K are needed. Therefore, the objective of this study was to improve the estimation of K at field scale through consideration of spatial dependences between soil variables. K was measured at 48 locations in a 71 × 71-m grid within a farmland under no-till. An autoregressive state-space approach was used to quantify the spatial relations between K and soil properties and to analyze the spatial variability of K in the field. In comparison, multiple linear regression (MLR) was used to derive PTFs for K estimation. Using various combinations of variables, state-space analysis outperformed PTFs in estimating spatial K distribution across the field. While state-space approach explained 69%, MLR method explained only 6% of the total variation in Ks. For K−10, the best state-space model included silt, clay, and macroporosity and performed almost perfectly (R2 >95%) in characterizing the spatial variability of K−10. In that case, the best MLR-type PTF explained only 60% of the variation. The results indicate that, by considering the spatial relations between soil variables, state-space approach is an effective tool for analyzing the spatial variability of K at field scale.

中文翻译:

用状态空间方法估算田间土壤导水率

摘要 饱和 (Ks) 和近饱和导水率 (K-10) 及其空间变异性的精确描述对于理解包气带中的水/溶质输运很重要。但是,直接测量 K 是很费力的。或者,可以使用土壤传递函数 (PTF) 从易于测量的土壤特性中预测 K。由于 PTF 忽略了土壤变量之间的空间关系和协方差,因此当需要对 K 进行田间尺度估计时,它们的表现往往不尽如人意。因此,本研究的目的是通过考虑土壤变量之间的空间依赖性来改进田间尺度的 K 估计。在免耕的农田内,在 71 × 71 米网格中的 48 个位置测量了 K。使用自回归状态空间方法来量化 K 与土壤性质之间的空间关系,并分析田间 K 的空间变异性。相比之下,多元线性回归 (MLR) 用于导出 K 估计的 PTF。使用各种变量组合,状态空间分析在估计整个场的空间 K 分布方面优于 PTF。虽然状态空间方法解释了 69%,但 MLR 方法仅解释了 Ks 总变化的 6%。对于 K-10,最好的状态空间模型包括粉砂、粘土和大孔隙,并且在表征 K-10 的空间变异性方面表现得几乎完美(R2 > 95%)。在那种情况下,最好的 MLR 型 PTF 只能解释 60% 的变异。结果表明,通过考虑土壤变量之间的空间关系,
更新日期:2019-06-01
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