Development of a tailored combination of machine learning approaches to model volumetric soil water content within a mesic forest in the Pacific Northwest

To monitor the complex interdependency between soil moisture and productivity of Douglas-fir forests in the Pacific Northwest a dense low-cost network of soil moisture sensors was installed in association with 33 forest vegetation plots in a single 2260-ha watershed in western Oregon. Four sensors were installed in each plot to record volumetric soil water content and soil temperature 5 and 50 cm below the surface of mineral soil at 2-hr intervals from 2012 through 2018.Significant data gaps restricted the full utilization of the resulting dataset summarized as daily averages. A Self-Organizing Map neural network was applied to find clusters of plots with similar functional dependencies of soil temperature and soil moisture values. The results guided the subsequent assignment of the most suitable time series as input for plot-specific Distributed Time Delay Neural Networks that were trained to simulate the time series of volumetric water content in the two soil depths at 33 locations. In addition to contemporaneous inputs used in conventional neural networks, the application of Distributed Time Delay Neural Networks allows the incorporation of prior sections of the input time series data. The results show the high performance of the combined machine learning approaches that capitalize on the spatial data richness of the dense monitoring network. The correlation coefficient for the test data subsets averages 0.95 and the mean error equals 0.04 cm³/cm³. The correlation between time series at various plots was retained for the modeled data. A cross-comparison of the data revealed that 81% of the 5-cm soil moisture data and 95% of the 50-cm soil moisture data show less than 10% difference between the coefficients of determination prior to and after the gap-filling procedure. The long-term gap-filled soil moisture extremes correspond well with a high-resolution Palmer Drought Severity Index calculated for the area.