Imagery classification of stream stage to support ephemeral stream monitoring
Abstract. 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 (U.S.). We trained our algorithm using hourly field camera images from 2017–2023, and validated the image classifications with 15-minute continuous stage observations. We then used image classifications to perform quality control on the continuous stage time series. 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 change 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.
I find this article to be generally well-written, well-structured, and applies a transferrable methodology to classify stream conditions in ephemeral streams in a single study watershed. The authors support their claims and provide adequate figures to support their argument.
I did find that some of the discussion sections strayed beyond the scope of the study described in the introduction section to discuss other features of the watershed and ephemeral streams more broadly. The paper would be strengthened by focusing on its central contribution.
I did find that a limitation of the study was that it focused on a subset of images from a single site. The methodology was demonstrated and its performance evaluated against predictions from the National Water Model, but statements about its transferability to other locations or are undercut by the limited nature of the data.
I have minor comments regarding clarity and a few considerations not in the original text but overall find the article a suitable contribution to HESS: