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 (r_m,Kosugi, σ_Kosugi) 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 (r_m,Kosugi) and pore radius standard deviation (σ_Kosugi). 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 r_m,Kosugi) while decreasing pore heterogeneity (lower σ_Kosugi). A drying period with high cumulative values of ET₀ or a strong climatic water balance deficit on the contrary reduced r_m,Kosugi while slightly increasing σ_Kosugi 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.

干湿 (WD) 循环显着影响结构相关的土壤特性和过程。大多数关于 WD 效应的研究都是基于实验室条件下的受控循环。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 水力特性模型的参数 ( r _{m, Kosugi} , σ _Kosugi ) 是通过张力渗透计测量值的逆向优化推导出来的。光谱分析用于从含水量时间序列计算 WD 循环强度、数量和持续时间。WD 周期强度是最好的预测因子 ( r ² = 0.53-0.57），用于在中值孔半径（时间漂移ř_米，小杉）和孔半径的标准偏差（σ_小杉）。在较低的土壤湿度条件下，循环强度的影响降低。使用WD强度和干燥期的气象指标（ET ₀，气候水平衡亏缺）作为预测变量导出双变量回归模型。该模型表明，WD 增强了大孔隙度（较高的r _m,Kosugi），同时降低了孔隙的非均质性（较低的σ _Kosugi）。ET ₀累积值高的干燥期或强烈的气候水平衡赤字反而降低了r _m,Kosugi同时由于小孔隙半径类别的频率较高，σ _Kosugi略有增加。应用二参数回归模型预测土壤孔径分布参数的时间过程。与具有平均水力参数的静态表示相比，通过计算值更好地捕获观察到的系统动力学。研究表明，光谱分析是量化野外 WD 模式的适当方法，WD 强度是土壤孔径分布时间动态的关键因素。