Search

• Experiments on flow resistance of foliated woody vegetation patches at reach-scale. • Reach-scale flow resistance controlled by patch geometry, less by canopy density. • Quantitative relationships for predicting flow resistance using blockage factors. Woody riparian vegetation typically clusters in patch form and increases flow resistance more significantly than individual plants. In this study, we examined the patchiness effects in non-submerged (emergent) conditions on the reach-scale flow resistance in field-scale experiments involving nature-like artificial willow patches. The patchiness characteristics of woody vegetation were systematically defined and classified using the canopy density, patch geometry described by cross-sectional and planar blockage areas, cross-sectional and volumetric blockage factors of the patches, and patch location in the cross-section. We developed quantitative relationships as empirical equations for estimating the vegetative flow resistance using blockage factors, that is, the area or volume fraction of canopy occupation. The results revealed that flow resistance due to emergent willow patches under relatively densely foliated and low volumetric blockage conditions was mostly explained by the blockage parameters and to a lower degree by the canopy density. In addition, it reveals the effects of the spatial distribution of flow and velocity by the relative position of patches, i.e., the flow resistance varies with different tendencies according to hydraulic conditions as the patch shifts from the channel centerline toward the bankside. This study provides reliable and practical relationships for estimating the flow resistance induced by riparian vegetation patches under reach-scale field conditions. [ABSTRACT FROM AUTHOR]

Water level data sets acquired by ultrasonic sensors in stream-scale channels exhibit relatively large numbers of outliers that are off the measurement range between the ultrasonic sensor and water surface, as well as data dispersion of approximately 2 cm due to random errors such as water waves. Therefore, this study develops a data processing algorithm for outlier removal and smoothing for water level data measured by ultrasonic sensors to consider these characteristics. The outlier removal process includes an initial cutoff process to remove outliers out of the measurement range and an outlier detection process using modified Z-scores based on the median absolute deviation (MAD) of a robust estimator. In addition, an exponentially weighted moving average (EWMA) method is applied to smooth the processed data. Sensitivity analyses are performed for factors that are subjectively set by the user, including the window size for the MAD outlier detection stage, the rejection criterion for the modified Z-score outlier removal stage, and the smoothing constant for the EWMA smoothing stage, based on four different water level data sets acquired by ultrasonic sensors in stream-scale experiments. [ABSTRACT FROM AUTHOR]