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Recently, increased attention has been devoted to intermittent and ephemeral streams (IRES) due to the recognition of their importance for ecology, hydrology, and biogeochemistry. However, IRES dynamics still demand further research, and traditional monitoring approaches present several limitations in continuously and accurately capturing river network expansion/contraction. Optical-based approaches have shown promise in noninvasively estimating the water level in intermittent streams: a simple setup made up of a wildlife camera and a reference white pole led to estimations within 2cm of accuracy in severe hydrometeorological conditions. In this work, we investigate whether the shortcomings imposed by adverse illumination can be partially mitigated by modifying this simple stage-cam setup. Namely, we estimate the image-based water level by using both the pole and a larger white bar. Further, we compare such results to those obtained with larger bars painted in the red, green, and blue primary colors. Our findings show that using larger white bars also increases reflections and, therefore, the accuracy in the estimation of the water level is not necessarily enhanced. Likewise, experimenting with colored bars does not significantly improve image-based estimations of the stage. Therefore, this work confirms that a simple stage-cam setup may be sufficient to monitor IRES dynamics, suggesting that future efforts may be rather focused on including filters and polarizers in the camera as well as on improving the performance of the image processing algorithm.

Abstract. Monitoring ephemeral and intermittent streams is a major challenge in hydrology. While direct field observations are best to detect spatial patterns of flow persistence, on site inspections are time and labor intensive and may be impractical in difficult-to-access environments. Motivated by latest advancements of digital cameras and computer vision techniques, in this work, we describe the development and application of a stage-camera system to monitor the water level in ungauged headwater streams. The system encompasses a consumer grade wildlife camera with near infrared (NIR) night vision capabilities and a white pole that serves as reference object in the collected images. Time-lapse imagery is processed through a computationally inexpensive algorithm featuring image quantization and binarization, and water level time series are filtered through a simple statistical scheme. The feasibility of the approach is demonstrated through a set of benchmark experiments performed in controlled and natural settings, characterized by an increased level of complexity. Maximum mean absolute errors between stage-camera and reference data are approximately equal to 2 cm in the worst scenario that corresponds to severe hydrometeorological conditions. Our preliminary results are encouraging and support the scalability of the stage-camera in future implementations in a wide range of natural settings.

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IntroductionMonitoring water levels of ephemeral streams is a difficult yet important task in hydrology, especially when studying minor river flows in remote areas. The installation of flow gauging stations on upstream tributaries is impacted by the lack of economic resources, by accessibility problems and unstable morphological conditions of riverbeds avoiding the implementation of distributed observation networks at large scales. This major challenge in hydrology may be addressed by eventually adopting image-analysis approaches that constitute an effective parsimonious river flow monitoring method, but the demonstration of such techniques is still an open research topic.MethodologyThis study focuses on the testing of a novel technique that employs a white pole “staff gauge” to be photographed using a phototrap (i.e., named stage-cam which is a high-speed camera trigger system). This technology shows to be particularly efficient for observing flood events that represent the most difficult scenario for streamflow monitoring. Furthermore, the testing of this innovative hydrological data-gathering method is performed by adopting citizen science and participatory image analysis to assess the value and effectiveness of non-expert volunteers to operationalize this novel method. Citizen engagement may be essential for supporting distributed flow monitoring supporting large scale image analysis algorithm calibration associated to a continuous series of phototrap images. The Montecalvello watershed, located near Rome, is selected for this pilot case study.ResultsResults of the conducted tests, involving the University of Tuscia student community, are presented toward the demonstration of the effectiveness of citizen science to collect valid quantitative hydrological observations, which may correlate consistently with expert estimates. To better interpret results, the authors consider mean absolute error (MAE) and mean absolute relative error (MARE) as synthetic indices to determine the uncertainties associated to voluntary observations. Low margins of error return positive feedback on the adopted methodology.DiscussionThis research promotes the use of participatory approaches for addressing an actual hydrological monitoring challenge. In addition, it fosters increased citizen knowledge and awareness of the importance and value of hydrological monitoring of small ungauged river basins.