Fuzzy-logic indicators for riverbed de-clogging suggest ecological benefits of large wood - Sebastian Schwindt; Beatriz Negreiros; Maria Ponce; Isabella Schalko; Simone Lassar; Ricardo Barros; Stefan Haun (Ecological Indicators, 2023)
Rivers provide dynamic habitats with ecological niches, particularly in their mobile sand, gravel, and cobble riverbed patches that create an active hyporheic zone. Natural or artificial deposition of fine sediment may clog the porous matrix of the hyporheic zone, impairing exchange processes between the subsurface and surface water. Clogging reduces the permeability of the sediment matrix, thus degrading the ecological functionality of the hyporheic zone. Once clogged, the ecological functions may be recovered through active stream restoration, which requires considerate site assessment. To this end, clogging is typically assessed by expert opinion of substrate characteristics including grain size characteristics, porosity, hydraulic conductivity, and interstitial oxygen content. To overcome limitations of expert assessment, such as subjectivity expressed in noisy decision-making, this study introduces a novel fuzzy-logic method based on physically sound rules. The method provides quantitative indicators for clogging and declogging to evaluate the effectiveness of stream restoration. We applied the fuzzy-logic method to test whether the placement of large wood, a common restoration practice, can locally prevent or reduce clogging. Two measurement series from before and after a morphologically effective flood suggested that large wood placements perpendicular to the flow generate elevated amounts of declogging. The tested logs caused a greater amount of declogging within their region of influence than observed at a reference point. The effect was stronger for a log emergent at baseflow. The declogging assessment showed that the novel fuzzy-logic indicators can reasonably overcome subjective judgment by accounting for multi-variate quantitative changes rather than individual parameter trends.