Automatic water segmentation often relies on supervised deep learning, which requires large, annotated datasets. We investigate deep learning approaches for water segmentation and, consequently, water stage estimation that need minimal or non-annotated datasets. We evaluated the Space-Time Correspondence Network or STCN, Segment Anything (SAM), and a combination of SAM with Grounding DINO. We used images from three stationary camera gauges and one dynamic river image dataset captured with an unmanned aerial vehicle (UAV). Further, we retrieved the water level using the produced water marks for the static images. For the UAV dataset, our results show that STCN and SAM achieved similar performances for image segmentation. Our findings reveal that the models can produce realistic water masks and water stage measurements. STCN achieved the best results for the camera gauges, making it a suitable option for sub-hour monitoring. Our approaches can be viable tools for ad hoc water stage measurements.