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Field quantification of wetting–drying cycles to predict temporal changes of soil pore size distribution

机译:干湿循环的现场量化以预测土壤孔径分布的时间变化

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摘要

Wetting–drying (WD) cycles substantially influence structure related soil properties and processes. Most studies on WD effects are based on controlled cycles under laboratory conditions. Our objective was the quantification of WD cycles from field water content measurements and the analysis of their relation to the temporal drift in the soil pore size distribution. Parameters of the Kosugi hydraulic property model (rm,Kosugi, σKosugi) were derived by inverse optimization from tension infiltrometer measurements. Spectral analysis was used to calculate WD cycle intensity, number and duration from water content time series. WD cycle intensity was the best predictor (r2 = 0.53–0.57) for the temporal drift in median pore radius (rm,Kosugi) and pore radius standard deviation (σKosugi). At lower soil moisture conditions the effect of cycle intensity was reduced. A bivariate regression model was derived with WD intensity and a meteorological indicator for drying periods (ET0, climatic water balance deficit) as predictor variables. This model showed that WD enhanced macroporosity (higher rm,Kosugi) while decreasing pore heterogeneity (lower σKosugi). A drying period with high cumulative values of ET0 or a strong climatic water balance deficit on the contrary reduced rm,Kosugi while slightly increasing σKosugi due to higher frequency at small pore radius classes. The two parameter regression model was applied to predict the time course of soil pore size distribution parameters. The observed system dynamics was captured substantially better by the calculated values compared to a static representation with averaged hydraulic parameters. The study showed that spectral analysis is an adequate approach for the quantification of field WD pattern and that WD intensity is a key factor for the temporal dynamics of the soil pore size distribution.
机译:干湿循环(WD)在很大程度上影响与结构相关的土壤特性和过程。关于WD效应的大多数研究都是基于实验室条件下的受控周期。我们的目标是从田间含水量测量中量化WD循环,并分析它们与土壤孔径分布的时间漂移​​的关系。 Kosugi水力学模型的参数(rm,Kosugi,σKosugi)是通过张力渗透仪测量得到的逆向优化得出的。光谱分析用于根据水含量时间序列计算WD循环强度,数量和持续时间。 WD周期强度是中值孔半径(rm,Kosugi)和孔半径标准偏差(σKosugi)随时间漂移的最佳预测因子(r 2 = 0.53-0.57)。在较低的土壤湿度条件下,循环强度的影响会降低。得出了以WD强度和干燥期的气象指标(ET0,气候水平衡不足)作为预测变量的双变量回归模型。该模型表明WD增强了大孔隙度(较高的rm,Kosugi),同时降低了孔的异质性(较低的σKosugi)。相反,由于小孔隙半径等级的频率较高,干燥期的ET0累积值高或气候水平衡亏空强烈,导致rm,Kosugi降低,而σKosugi则略有增加。应用两参数回归模型预测土壤孔径分布参数的时程。与具有平均水力参数的静态表示相比,通过计算值可以更好地捕获观察到的系统动力学。研究表明,光谱分析是定量田间WD模式的适当方法,并且WD强度是土壤孔径分布随时间变化的关键因素。

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