The increasing frequency of extreme hydrological events, driven by climate change, necessitates dense and scalable water level monitoring networks. We evaluate a low-cost, non-contact, stereo-vision camera system for automated water level estimation. We compare two distinct image-processing pipelines — with and without semantic masking — to determine their camera pose stability and measurement accuracy. Our results show that raw stereo-vision estimates are highly correlated with reference sensor measurements (correlations ranging from 0.70 to 0.77), capturing the overall hydrologic behavior. By implementing a masking technique to isolate static environmental features, we successfully corrected a baseline error (i.e., offset), aligning the system with the true physical geometry. Although masking improves absolute accuracy, it introduces transient instability (i.e., spikes in pose estimation). This study serves as a proof of concept for the deployment of low-cost, edge-based stereo-vision systems in hydrological monitoring.