Image-based classification of stream stage to support ephemeral stream monitoring

Intermittent rivers and ephemeral streams (IRES) constitute a large fraction of global river networks, provide important ecosystem services, and are increasing in number with climate change. Yet, observing stage and calculating discharge in IRES can be technologically and methodologically challenging. To address this problem, we develop a method to classify relative stage categories from field camera imagery, creating a time series of categorical flow states without the need for direct stage measurements. Specifically, we employ a Logistic Regression model to classify conditions of no water, low water levels, or high water levels for an ephemeral stream located in the upper Russian River watershed of California (US). We trained our algorithm using hourly field camera images from 2017–2023, and validated the image classifications with 15 min continuous stage observations. We then used image classifications to perform quality control on the continuous stage time series, which allowed us to identify when the stream was dry and when the sensor malfunctioned. Next, we compared the image classifications to publicly accessible modeled discharge from the NOAA National Water Model CONUS Retrospective Dataset. We discuss how in-situ monitoring including field cameras and the classification of field camera imagery, combined with surface meteorology and soil moisture observations, provides detailed hydrologic information important for understanding how climate affects IRES. Because the image classification approach is transferable to other ephemeral stream sites equipped only with field cameras, this methodology provides a low-cost option for observing relative stage on sparsely-measured IRES that can augment existing hydrologic modeling used by water managers.