Testing the theoretical principles of citizen science in monitoring stream water levels through photo-trap frames

Abstract

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.