How trees influence the spatiotemporal dynamics of soil moisture in different forest ecosystems

Abstract. Soil moisture (SM) is a key variable in terrestrial ecosystems, regulating water and energy exchange at the soil-atmosphere interface. In forest ecosystems, SM exhibits pronounced spatiotemporal variability at the tree-scale in the topsoil of the active root zone due to complex interactions among multiple factors such as soil properties, topography, climate, and vegetation. Existing research on spatiotemporal SM characteristics in forests is limited, impeding our understanding of SM-vegetation interactions. A detailed understanding of when and to what extent vegetation influence topsoil moisture spatiotemporal variability in forests is required to quantify its consequences for (eco‑)hydrological processes and to test hypotheses about the dominating influences on spatiotemporal patterns across different ecosystems.

This study characterizes spatiotemporal dynamics of topsoil SM. Our objectives are to quantify plot-scale spatial variability and its temporal evolution from event- to seasonal scale, thereby improving our understanding of SM dynamics across wet and dry states in pure- and mixed-species forest stands. We recorded SM with a dense monitoring network of 400 sensors (SMT100, Truebner GmbH, Germany) installed in the topsoil in a tree-centered design across four plots of mixed and pure Douglas fir, Beech and Silver fir trees in the ECOSENSE forest, southwestern Germany. Statistical and Empirical Orthogonal Function (EOF) analysis together with temporal stability (TS) analysis were combined to quantify spatial SM dynamics from a 2.5-year dataset (2023–2025). Spatial SM follows a consistent annual cycle across all plots transitioning between wet- and dry-preferential states. SM shows high variability for all plots and most plots display a characteristic convex upward curve for the spatial mean soil moisture to coefficient of variation (SM̄–CV) relationship. The CV peaks at intermediate wetness and declines toward the driest and wettest states. Only the Mixed plot shows a linear, negative SM̄–CV relationship. Daily SM skewness follows the same convex relation with negative skew for wet conditions (SM̄ > 20  %) and positive skew for dry conditions. The EOF analysis identified two to three statistically significant modes per plot, together explaining > 80 % of spatial variance. The first EOF explains > 60 % of the variance at each plot and represents the dominant, stable, time-invariant spatial pattern. Seasonal wetting and drying cycles show that spatial variability differs between wet and dry phases, suggesting distinct local and non-local controls dominate the SM patterns according to the wetness state and seasonal phase of the system, increasing or decreasing spatial SM heterogeneity. The temporal stability analysis identified persistently wetter and drier locations. Mean relative difference (MRD) within each plot ranges from –75 % (dry) to +50 % (wet), with the widest range in the Silver fir plot and the narrowest range in the Beech plot. Within each plot, a subset of representative locations could be identified, but the exact location in the plot is random. Spearman rank correlations of spatial SM between the Douglas fir and Beech plot show high spatial stability (ρ ≥ 0.8) for wet phases (winter) whereas lower correlation (ρ ≈ 0.45–0.60) during dry phases. The findings of this study can help to inform sampling strategies and modelling approaches. The dataset presented here is appropriate for detailed analyses of event-based wetting-drying dynamics, and when combined with additional data, it can disentangle the relative contributions of local and non-local controls to the observed spatial patterns and pattern stability.