Integrated hydrological models are usually calibrated against observations of river discharge and piezometric head in groundwater aquifers. Calibration of such models against spatially distributed observations of river water level can potentially improve their reliability and predictive skill. However, traditional river gauging stations are normally spaced too far apart to capture spatial patterns in the water surface, while spaceborne observations have limited spatial and temporal resolution. UAVs (Unmanned Aerial Vehicles) can retrieve river water level measurements, providing: i) high spatial resolution; ii) spatially continuous profiles along or across the water body; iii) flexible timing of sampling. A semi-synthetic study was conducted to analyse the value of the new UAV-borne datatype for improving hydrological models, in particular estimates of GW (Groundwater)- SW (Surface Water) interaction. Mølleåen River (Denmark) and its catchment were simulated using an integrated hydrological model (MIKE 11-MIKE SHE). Calibration against distributed surface water levels using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm demonstrated a significant improvement in estimating spatial patterns and time series of GW-SW interaction. After water level calibration, the sharpness of the estimates of GW-SW time series improves of ca. 50% and RMSE (Root Mean Square Error) decreases by ca. 75% compared to a model calibrated against discharge only.