Hillslope hydrological dynamics, particularly subsurface flow (SSF), are highly variable and complex. A profound understanding of factors controlling this variability is needed. Therefore we investigated the relationship between variability of shallow water table dynamics and various hillslope characteristics. We ask whether measurable hillslope properties explain patterns of subsurface flow variability. To approach this question, shallow water table dynamics of three adjacent large-scale hillslopes were monitored with high spatial and temporal resolution over 18 months. The hillslopes are similar in terms of topography and parent material, but different in vegetation cover (grassland, coniferous forest, and mixed forest). We expect vegetation to be an important driver of water table dynamics at our study site, especially given the minor differences in topography. Various hillslope properties were determined in the field and via GIS analysis: common topography descriptors, well depth, soil properties via slug tests, and several vegetation parameters. Response variables characterizing the water table response per well were calculated for different temporal scales (entire time series, seasonal scale, event scale). Partial correlation analysis and a Random Forest machine learning approach were carried out to assess the explainability of SSF variability by measurable hillslope characteristics. We found a complex interplay of predictors, yet soil properties and topography showed the highest single explanatory power. Surprisingly, vegetation characteristics played a minor role. Solely throughfall and canopy cover exerted a slightly stronger control, especially in summer. Most importantly, the examined hillslope characteristics explained only a small proportion of the observed SSF variability. Consequently there must be additional important drivers not represented by current measurement techniques of the hillslope configuration (e.g. bedrock properties, preferential pathways). We also found interesting differences in explainability of SSF variability among temporal scales and between both forested hillslopes and the grassland hillslope.