Soil vegetation atmosphere transport (SVAT) models are important for the quantification of water fluxes, soil water availability, drought stress and their uncertainties under climate change. We present LWFBrook90R, an enhanced implementation of the well-established, process-based SVAT model LWF-Brook90 for the R environment for statistical computing. The package provides new functions and sub-models for model parameterization, and facilitates parallel computing, sensitivity analysis and inverse calibration of the model. A case study comprising i) basic forward water balance simulations for temperate grassland vegetation, deciduous and evergreen forest, ii) a parallelized sensitivity analysis, and iii) Bayesian calibrations based on soil water storage observed in a poplar (Populus nigra × P. maximowiczii) Short Rotation Forest (SRF) demonstrates the utility of the R package. The sensitivity analysis revealed parameters affecting plant-available soil water storage capacity and the vegetation's timing and level of water demand to be most important for the annual course of simulated soil water storage, with seasonal and interannual differences in parameter importance rankings. The subsequent calibration yielded a very high agreement between daily simulated and observed soil water storage (0-200 cm soil depth) for the calibration and validation datasets, with Nash-Sutcliffe efficiencies of 0.97 and 0.95, respectively. The final model predicted high though realistic rates of annual evapotranspiration (2011: 844 ± 3.8 mm y^-1, 2012: 733 ± 4.5 mm y^-1) for the poplar SRF, regularly exceeding grass reference evaporation (ET₀) by 20-47% during the months of the growing season. However, basing calibrations solely on observed soil water storage probably resulted in biased partitioning of evapotranspiration towards interception losses. The integration of the LWF-Brook90 hydrological model into R with its wide variety of extensions was successfully tested and may provide efficient, reliable and reproducible water balance predictions by facilitating complex statistical analyses and large-scale applications of the model.