Flow distribution in meandering compound channels

Magnitude of flood prediction is the fundamental for flood warning, determining the development for the present flood- risk areas and the long-term management of rivers. Discharge estimation methods currently employed in river modeling software are based on historic hand calculation formulae such as Chezy’s, Darcy-Weisbatch or Manning’s equation. More recent work has provided significant improvements in understanding and calculation of channel discharge. This ranges from the gaining knowledge to interpretation of the complex flow mechanisms to the advent of computing tools that enable more sophisticated solution techniques. When the flows in natural or man made channel sections exceed the main channel depth, the adjoining floodplains become inundated and carry part of the river discharge. Due to different hydraulic conditions prevailing in the river and floodplain, the mean velocity in the main channel and in the floodplain are different. Just above the bank-full stage, the velocity in main channel is much higher than the floodplain. Therefore the flow in the main channel exerts a pulling or accelerating force on the flow over floodplains, which naturally generates a dragging or retarding force on the flow through the main channel. This leads to the transfer of momentum between the main channel water and that of the floodplain. The interaction effect is very strong at just above bank full stage and decreases with increase in depth of flow over floodplain. The relative “pull” and “drag” of the flow between faster and slower moving sections of a compound section complicates the momentum transfer between them. Failure to understand this process leads to either overestimate or underestimate the discharge leading to the faulty design of channel section. This causes frequent flooding at its lower reaches. Due to transfer of momentum between the subsections of the meandering compound channel, the shear distribution is largely affected. For such compound channels, the apparent shear force at the assumed interface plane gives an insight into the magnitude of flow interaction. The results of some experiments concerning the velocity distribution and the flow distribution in a smooth and rough compound meandering channel of rectangular cross section are presented. The influence of the geometry on velocity and flow distribution and different functional relationships are obtained. Dimensionless parameters are used to form equations representing the velocity distribution and flow distribution between main channel and flood plain subsections. Once these equations get formed one can judge the exact flow in main channel and flood channel sections which could possibly guide in flood prediction. The experiments concerning the flow in simple meander channels and meander channel - floodplain geometry have been conducted at the Fluid Mechanics and Water Resources Engineering Laboratory of the Department Civil Engineering, National Institute of Technology, Rourkela, India. Channels of different shapes and sizes have been fabricated in the laboratory with different equipments installed in them. Water is allowed to flow through these channels and the flow is maintained smooth. The Acoustics Doppler Velocitimeter (ADV) installed in the lab is worth mentioning. Taking the aid of a laptop terminal, this equipment helps in determining the three- dimensional velocities (Vx, Vy, Vz) at any point in the water channel. All the velocity readings obtained are recorded and finally velocity contours (i.e. isovels) are plotted with a software 3D-Field. Depending on the flow pattern and shape of the channel, contours are obtained. All the contours are converted to bitmap image and finally inputted in MATLAB software. Now with this software discharge through a channel cross-section is generated which when compared to the actual flow discharge gives a very less percentage of error. Finally equations related to the flow distribution are formed based on the given datas. These formed equations are validated with datas collected from IIT Kharagpur (Bhattacharya, A. K. (1995) and those from Knight and Demetriou( Knight, D.W., and Demetriou, J.D., (1983) which satisfies them as well

Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review

International audienceThe study of mass and energy transfer across landscapes has recently evolved to comprehensive considerations acknowledging the role of biota and humans as geomorphic agents, as well as the importance of small-scale landscape features. A contributing and supporting factor to this evolution is the emergence over the last two decades of technologies able to acquire high resolution topography (HRT) (meter and sub-meter resolution) data. Landscape features can now be captured at an appropriately fine spatial resolution at which surface processes operate; this has revolutionized the way we study Earth-surface processes. The wealth of information contained in HRT also presents considerable challenges. For example, selection of the most appropriate type of HRT data for a given application is not trivial. No definitive approach exists for identifying and filtering erroneous or unwanted data, yet inappropriate filtering can create artifacts or eliminate/distort critical features. Estimates of errors and uncertainty are often poorly defined and typically fail to represent the spatial heterogeneity of the dataset, which may introduce bias or error for many analyses. For ease of use, gridded products are typically preferred rather than the more information-rich point cloud representations. Thus many users take advantage of only a fraction of the available data, which has furthermore been subjected to a series of operations often not known or investigated by the user. Lastly, standard HRT analysis work-flows are yet to be established for many popular HRT operations, which has contributed to the limited use of point cloud data.In this review, we identify key research questions relevant to the Earth-surface processes community within the theme of mass and energy transfer across landscapes and offer guidance on how to identify the most appropriate topographic data type for the analysis of interest. We describe the operations commonly performed from raw data to raster products and we identify key considerations and suggest appropriate work-flows for each, pointing to useful resources and available tools. Future research directions should stimulate further development of tools that take advantage of the wealth of information contained in the HRT data and address the present and upcoming research needs such as the ability to filter out unwanted data, compute spatially variable estimates of uncertainty and perform multi-scale analyses. While we focus primarily on HRT applications for mass and energy transfer, we envision this review to be relevant beyond the Earth-surface processes community for a much broader range of applications involving the analysis of HRT

Catchment topography : improving hydrologic predictions with lidar analysis

Channels, floodplains, hillslopes, and ridges are characteristic topographic features of landscapes around us. These topographic features occur at a variety of spatial scales. Climate, vegetation, soil type, and terrain characteristics control the shape of a catchment and of the channel network. Increasingly extreme and unpredictable weather patterns demand for better prediction of catchment hydrologic responses. The key to predict catchment response lies in understanding the topographic patterns and how they are influenced by the underlying processes, climate, and anthropogenic modifications. With the availability of high resolution topographic data, the characterization of topographic features at the scales relevant to hydrology and geomorphic processes is now possible. Light Detection and Ranging (lidar) digital terrain models (DTMs) (meter and sub-meter resolution) allow us to accurately quantify patterns of landscape dissection (e.g., drainage density), channel head locations, surface runoff patterns and hillslope length scales. Coarse resolution datasets (30- 100m), such as Shuttle Radar Topographic Mission (SRTM), fail to capture local variability at relevant process scales and only resolve large scale topographic patterns. As we continue to collect high resolution data there is a growing need to develop new methods and algorithms to objectively extract topographic features, such as channels, and identify metrics able to characterize topography over large areas. The goals of the research presented here are to (i) identify the signature of climate, vegetation, topography and lithology on channel patterns, (ii) define new metrics to quantify catchment topography across a range of scales, (iii) improve existing feature extraction techniques for channel networks to upscale them to handle larger catchments, and, (iv) develop feature extraction tools for urban and highly engineered setting. The research will deepen our understanding about the effects of climate on channel patterns across a range of scales; the identification of new metrics will help characterize landscapes in an objective manner; improvement of existing feature extraction techniques to handle large catchments will help in designing best management practices for watersheds through distributed mapping of topographic attributes such as slope, curvature, and accumulation area; feature extraction in urban and engineered settings will improve the analysis of watersheds modified by humans.Civil, Architectural, and Environmental Engineerin

Point cloud classification for water surface identification in Lidar datasets

textLight Detection and Ranging (Lidar) is a remote sensing technique that provides high resolution range measurements between the laser scanner and Earth’s topography. These range measurements are mapped as 3D point cloud with high accuracy (< 0.1 meters). Depending on the geometry of the illuminated surfaces on earth one or more backscattered echoes are recorded for every pulse emitted by the laser scanner. Lidar has the advantage of being able to create elevation surfaces in 3D, while also having information about the intensity of the returned pulse at each point, thus it can be treated as a spatial and as a spectral data system. The 3D elevation attributes of Lidar data are used in this study to identify possible water surface points quickly and efficiently. The approach incorporates the use of Laplacian curvature computed via wavelets where the wavelets are the first and second order derivatives of a Gaussian kernel. In computer science, a kd-tree is a space-partitioning data structure used for organizing points in a k dimensional space. The 3D point cloud is segmented by using a kd-tree and following this segmentation the neighborhood of each point is identified and Laplacian curvature is computed at each point record. A combination of positive curvature values and elevation measures is used to determine the threshold for identifying possible water surface points in the point cloud. The efficiency and accurate localization of the extracted water surface points are demonstrated by using the Lidar data for Williamson County in Texas. Six different test sites are identified and the results are compared against high resolution imagery. The resulting point features mapped accurately on streams and other water surfaces in the test sites. The combination of curvature and elevation filtering allowed the procedure to omit roads and bridges in the test sites and only identify points that belonged to streams, small ponds and floodplains. This procedure shows the capability of Lidar data for water surface mapping thus providing valuable datasets for a number of applications in geomorphology, hydrology and hydraulics.Civil, Architectural, and Environmental Engineerin