Flood risks have been increasing in recent years due to climate change. To provide flood risks through simulations near river areas, observational data of river discharges is required. However, the information of river discharges is sparse because of the limited availability of gauge stations. In this work, we use a combination of topographic features in location-scale with meteorological features in basin-scale to predict river discharges in ungauged locations along a river network. We utilize deep kernel learning as our core model and use non-stationary kernel formulation to enable spatiotemporal interpolation. We tested the performance in two different rivers using one year of collected data and compared it with several baselines. According to our experimental results, our proposed model improved Nash Efficiency Criterion (NSE) by 2.6%, normalized root mean-squared error (NRMSE) by 7%, and coefficient of determination (R2) by 275% compared with ordinary kriging as the baseline. The combination of features used in this experiment also improved NSE by 27.1%, NRMSE by 13.7%, and R2 by 182.6%.