Search

Reach-scale experiments simulating the physical characteristics of natural woody plants are required for the improved modeling of flow resistance in channels with instream and floodplain vegetation. The first aim of this study was to investigate the flow resistance caused by patches of foliated woody vegetation at low volumetric blockage factors (4% to 10%) under emergent conditions, as most studies on the effect of patchiness are limited to submerged aquatic plants. Experiments with nature-like willow patches were performed in a reach-scale outdoor flume, where the density and layout of the patches were altered. The bulk friction factors were based on the friction slopes, calculated using the water surface slopes and velocity heads measured by a set of high-accuracy pressure sensors under several flow conditions. Compared to unvegetated conditions, the patches increased the bulk friction factor by 1.7–5.5 times, depending on the patch density and blockage factor. The second aim of this study was to evaluate the performance of four momentum-based models in estimating the bulk friction factors in flows with such flexible patches. For this process, a methodological approach suited for patchy woody vegetation at reach scale was developed to determine the input parameters required in each model. Estimation results using the newly modified and parameterized formulas compared favorably to the experimental data, demonstrating more reliable performance for models explicitly addressing the reconfiguration of vegetation. Overall, this study provides a practical methodology for estimating flow resistance caused by complex distributions of woody riverine vegetation. [ABSTRACT FROM AUTHOR]

Vegetation notably influences transport and mixing processes and can thus be used for controlling the fate of substances in the hydro‐environment. Whilst most work covers fully vegetated conditions, the novelty of this paper is to focus on flows with real‐scale flexible willow patches. We aimed to investigate how longitudinal dispersion varies according to the spatial distribution, density and coverage of the patches and to evaluate the explanatory power of predictors that consider the hydraulics, vegetation and channel geometry. Salt tracer experiments were performed in a trapezoidal channel where we established 3–4 m long and 1–1.6 m wide patches of artificial foliated willows that reproduced the shapes and plant densities observed on woody‐vegetated floodplains. We examined sparsely distributed patches with low areal/volumetric coverage of 6–11%, and non‐vegetated conditions for reference. Flow depths and surface widths were 0.7–0.9 and 6–7 m, respectively, and the mean flow velocities ranged at 0.3–0.6 m/s. The emergent patches generated from a negligible to over a four‐fold increase in the longitudinal dispersion when compared with non‐vegetated conditions. The patches with a preferential location in low‐velocity areas, such as near banks, or with a high plant density and a blockage of the cross‐sectional flow area ⪆0.4, led to the largest dispersion and residence times. Patches under such configurations enhanced the normalized differential velocity defined as the difference between the highest (90th percentile) and lowest (10th percentile) cross‐sectional flow velocities divided by the mean velocity, thus increasing shear dispersion. As existing analytical predictors failed to estimate the effect of different patch configurations, we proposed the change in the normalized differential velocity between vegetated and corresponding non‐vegetated conditions as a basic predictor of the reach‐scale longitudinal dispersion coefficient under patchy vegetation. In contrast, we observed no clear relationship between flow resistance and dispersion. Thus, our findings indicated that bankside vegetation may allow for reduced peak concentrations and lengthened residence times, supporting pollutant management, while ensuring good flow conveyance. Such rare field‐scale analyses improve the estimation of solute transport in real vegetated flows. [ABSTRACT FROM AUTHOR]

The records of 24,797 traffic accidents (9039 involving fatalities or severe injury) during rainy conditions from 2007 to 2017 in Seoul, South Korea, were used to analyze the spatial distribution of the traffic accidents and rainfall events based on radar and gauge rainfall data. According to the spatial correspondence analysis between rainfall distribution and accident locations for localized and stratiform rain events, radar data in a two-dimensional grid (250 by 250 m) of 10 min temporal resolution benefited the localized rainfall distribution concerning the accident location. The relative accident rate (RAR) from radar data, which was used as a quantitative reference value for the effect of rainfall on traffic accidents, was about 18% higher than that from gauge rainfall. The radar data more clearly classified the number of traffic accidents during rainy conditions because its spatial distribution was more precise for all accidents. In addition, the RAR estimation of accidents involving fatalities and severe injury during rainfall could provide information on the district in which traffic accidents increase due to rainfall. The study results support the adoption of radar-derived rainfall data to analyze the influence of rainfall on accidents and the development of more accurate risk-assessment tools for drivers and planners. [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]