Effects of spatial soil moisture variability in forest plots on model parametrization and simulated groundwater recharge estimates

Abstract

Soil-Vegetation-Atmosphere Transfer (SVAT) models are essential tools for simulating and underploting the dynamic interactions governing water balance components within forest ecosystems. These models are widely employed to predict hydrological responses to environmental change, including the impacts of shifting meteorological conditions on forested landscapes. Despite their usefulness, the reliability of SVAT models is frequently compromised by uncertainties arising from incomplete or imprecise input data. These limitations often result in model assumptions that may lead to over- or underestimation of critical water balance components such as groundwater recharge. In order to improve the accuracy of SVAT models, observed soil moisture data are integrated to enhance parameterization processes by aligning simulated outputs with measured values. However, uncertainties remain regarding the selection of representative soil moisture profiles for calibration and the extent of measurements necessary to robustly characterize a forest plot. To address these challenges, the present study explores the spatial variability of soil moisture across two forested plots with contrasting soil and vegetation conditions by the deployment of an extensive network of soil moisture probes in 11 profiles per plot. The influence of soil moisture variability on the adjustment of model input parameters during the calibration process and its subsequent impact on the computation of groundwater recharge is evaluated. The findings reveal that soil moisture variability at the plot characterized by a heterogeneous soil was greater, both horizontally and in depth, throughout the study period. These patterns of variability are also mirrored in the different parameter sets obtained from the calibration of the LWF Brook90 model, based on the recorded soil moisture time series in each of the 11 profiles per plot. The most significant variation is observed in the infiltration and hydraulic soil parameters, whereby this is more pronounced at the plot with heterogeneous soil structure. When examining the groundwater recharge rates calculated using the 30 best-performing parameter sets for each of the 11 profiles, both plots exhibited comparable temporal patterns but substantial differences in total groundwater recharge volumes across profiles. The results also suggest that model-inherent uncertainties, including parameter interactions, equifinality and dimensional simplifications, have a stronger impact on model outputs than uncertainties arising from variability in soil moisture caused by spatial heterogeneity of soil texture and hydraulic properties within the plot. Taking into account both sources of uncertainty, the application of Monte Carlo based subsampling yielded contrasting results. While groundwater recharge at the Kienhorst plot could be reliably estimated using data from only 3 soil profiles at the investigated 20×20 m plot, eleven soil profiles were required at the Tharandt site to adequately capture the spatial variability of the system. These numbers are indicative rather than universal thresholds, as the required sampling density is strongly site-specific and depends on local soil structure and model related uncertainties.