Abstract. The fields of eco-hydrological modelling and extreme flow prediction and management demand for detailed information of streamflow intermittency and its corresponding landscape controls. Innovative sensing technology for monitoring of streamflow intermittency in perennial rivers and intermittent reaches improve data availability, but reliable maps of streamflow intermittency are still rare. We used a large dataset of streamflow intermittency observations and a set of spatial predictors to create logistic regression models to predict the probability of streamflow intermittency for a full year, and, wet and dry periods for the entire 247 km2 Attert catchment in Luxembourg. Similar climatic conditions across the catchment permit a direct comparison of the streamflow intermittency among different geological and pedological regions. We used spatial predictors describing land cover, track (road) density, terrain metrics, soil and geological properties as local as well as integral catchment information. The terrain metrics catchment area and profile curvature were the most important predictors for all models. However, the models which include the dry period of the year reveal the importance of soil hydraulic conductivity, bedrock permeability and in case of the annual model the presence of tracks (roads) during low flow conditions. A classification of spatially distributed streamflow intermittency probabilities into ephemeral, intermittent and perennial reaches allows the estimation of stream network extent under various conditions. This approach is a first step to provide detailed spatial information for hydrological modelling as well as management practice.