Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s − 1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s − 1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s − 1 of the ADCP measurements, on average.

The new simulation model, named SIPSON, based on the Preissmann finite difference method and the conjugate gradient method, is presented in the paper. This model simulates conditions when the hydraulic capacity of a sewer system is exceeded, pipe flow is pressurized, the water flows out from the piped system to the streets, and the inlets cannot capture all the runoff. In the mathematical model, buried structures and pipelines, together with surface channels, make a horizontally and vertically looped network involving a complex interaction of flows. In this paper, special internal boundary conditions related to equivalent inlets are discussed. Procedures are described for the simulation of manhole cover loss, basement flooding, the representation of street geometry, and the distribution of runoff hydrographs between surface and underground networks. All these procedures are built into the simulation model. Relevant issues are illustrated on a set of examples, focusing on specific parameters and comparison with field measurements of flooding of the Motilal ki Chal catchment (Indore, India). Satisfactory agreement of observed and simulated hydrographs and maximum surface flooding levels is obtained. It is concluded that the presented approach is an improvement compared to the standard "virtual reservoir" approach commonly applied in most of the models.

The paper presents the development of the field of urban drainage modelling known as dual drainage - an approach to rainfaill runoff simulation in which the numerical model takes into account not only the flow through the sewer system, but also the flow on the surface. The steps in model development are described, and necessary data, assumptions used and operations to be performed using GIS are discussed. The numerical model simultaneously handles the full dynamic equations of flow through the sewer system and simplified equations of the surface flow. The surface excess water (due to the limited capacity of inlets or to the hydraulic head in the sewer system reaching the ground level) is routed to the neighbour subcatchment (not necessarily the one attached to the downstream network node), using surface retentions, if any.

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Previous experimental research on the effects of debris on pier scour has focused primarily on circular and rectangular piers with debris present just under flow free surface. Debris-induced scour around sharp-nose piers, which are typical of masonry bridge piers, and the effect of debris elevation on pier scour have seldom been studied before. This paper aims to fill this knowledge gap. It presents results from flume experiments investigating scour around a sharp-nose pier under shallow flow conditions with angle of attack relative to the pier being zero. Uniform sand is used as bed material. Debris is modeled as stationary and extending only upstream of the pier. Three simplified debris geometries (cylinder, half-pyramid, and plate) are studied. Results show that scour depth decreases as debris gets closer to the bed with maximum scour depth occurring when debris is located just under the flow free surface. Interestingly, scour depths produced by debris in shallow flow are observed to be comparable to those produced by deep flow in the absence of debris. This finding highlights the importance of monitoring debris accumulation at bridges in nonflood conditions. Results also show that the volume of the scour hole around a pier increases quadratically with maximum scour depth. This information is useful for postflood scour remedial works. Lastly, the collected laboratory measurements are used to compare four popular equations for scour estimation on their ability to predict debris-induced scour. The Colorado State University (CSU) equation is found to offer the most accurate predictions. ; separate discussions must be submitted for individual papers. This paper is part of the Journal of Hydraulic Engineering, © ASCE, ISSN 0733-9429. © ASCE 04018071-1 J. Hydraul. Eng. J. Hydraul. Eng., 2018, 144(12): 04018071 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04018071-2 J. Hydraul. Eng. J. Hydraul. Eng., 2018, 144(12): 04018071 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04018071-5 J. Hydraul. Eng. J. Hydraul. Eng., 2018, 144(12): 04018071 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04018071-6 J. Hydraul. Eng. J. Hydraul. Eng., 2018, 144(12): 04018071 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04018071-8 J. Hydraul. Eng. J. Hydraul. Eng., 2018, 144(12): 04018071 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04018071-10 J. Hydraul. Eng. J. Hydraul. Eng., 2018, 144(12): 04018071 Downloaded from ascelibrary.org by 44.224.250.200 on 07/05/20. Copyright ASCE. For personal use only; all rights reserved. © ASCE 04018071-12 J. Hydraul. Eng.