Aljoumani, BasemSanchez-Espigares, Jose A.Wessolek, Gerd2019-02-182019-02-182018-12-13https://depositonce.tu-berlin.de/handle/11303/9124http://dx.doi.org/10.14279/depositonce-8213Despite the importance of computing soil pore water electrical conductivity (σp) from soil bulk electrical conductivity (σb) in ecological and hydrological applications, a good method of doing so remains elusive. The Hilhorst concept offers a theoretical model describing a linear relationship between σb, and relative dielectric permittivity (εb) in moist soil. The reciprocal of pore water electrical conductivity (1/σp) appears as a slope of the Hilhorst model and the ordinary least squares (OLS) of this linear relationship yields a single estimate ( 1/σpˆ ) of the regression parameter vector (σp) for the entire data. This study was carried out on a sandy soil under laboratory conditions. We used a time-varying dynamic linear model (DLM) and the Kalman filter (Kf) to estimate the evolution of σp over time. A time series of the relative dielectric permittivity (εb) and σb of the soil were measured using time domain reflectometry (TDR) at different depths in a soil column to transform the deterministic Hilhorst model into a stochastic model and evaluate the linear relationship between εb and σb in order to capture deterministic changes to (1/σp). Applying the Hilhorst model, strong positive autocorrelations between the residuals could be found. By using and modifying them to DLM, the observed and modeled data of εb obtain a much better match and the estimated evolution of σp converged to its true value. Moreover, the offset of this linear relation varies for each soil depth.en620 Ingenieurwissenschaften und zugeordnete Tätigkeitenelectrical conductivityrelative dielectric permittivitytime domain reflectometrykalman filterdynamic linear modelArticle1424-8220