Spatiotemporal dynamics of capillary rise in shallow groundwater table areas revealed by spatial TDR

Accurate quantification of soil water content (θ, cm 3 cm –3 ) dynamics and capillary rise in shallow groundwater table areas remains a major challenge due to the limitations of conventional discrete point-scale measurements of θ. This study presents an integrated monitoring sensor system based on spatial time domain reflectometry (TDR) for retrieving high-resolution θ profiles along transmission lines. The approach combines inverse modeling of TDR waveforms using a fast–iterative physics–based transmission line model with Python-based code for automated signal processing, dynamic calibration against co-installed low-cost θ sensors and uncertainty propagation. Calibration is further constrained using soil hydraulic properties of undisturbed soil samples, enabling physically consistent scaling of θ values to soil porosity, while detailed analysis of each uncertainty source quantifies 90% confidence internals in both θ profiles and threshold–based detection of the saturated portion of capillary fringe. Field application in a clay-rich experimental site revealed pronounced small-scale heterogeneity in groundwater table fluctuations and capillary rise dynamics. Spatial TDR measurements captured the upward–downward movement of the saturated portion of capillary fringe in response to diurnal groundwater fluctuations, providing direct observations of transient θ redistribution across the unsaturated zone. The results demonstrate that spatial TDR significantly improves the characterization of θ profile fluctuations compared to discrete measurements, resolving processes that are typically unobservable with conventional techniques. The proposed sensor system offers a robust methodology for linking groundwater dynamics with θ variability, providing a basis for precision in agricultural management, evapotranspiration estimation, and groundwater abstraction strategies. The integration of spatial monitoring with automated data processing provides a scalable pathway toward continuous, profile-scale observation of coupled surface–subsurface hydrological processes.