Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling

Flood modelling of urban areas is still at an early stage, partly because until recently topographic data of sufficiently high resolution and accuracy have been lacking in urban areas. However, digital surface models (DSMs) generated from airborne scanning laser altimetry (LiDAR) having sub‐metre spatial resolution have now become available, and these are able to represent the complexities of urban topography. This paper describes the development of a LiDAR post‐processor for urban flood modelling based on the fusion of LiDAR and digital map data. The map data are used in conjunction with LiDAR data to identify different object types in urban areas, though pattern recognition techniques are also employed. Post‐processing produces a digital terrain model (DTM) for use as model bathymetry, and also a friction parameter map for use in estimating spatially distributed friction coefficients. In vegetated areas, friction is estimated from LiDAR‐derived vegetation height, and (unlike most vegetation removal software) the method copes with short vegetation less than ～1 m high, which may occupy a substantial fraction of even an urban floodplain. The DTM and friction parameter map may also be used to help to generate an unstructured mesh of a vegetated urban floodplain for use by a two‐dimensional finite element model. The mesh is decomposed to reflect floodplain features having different frictional properties to their surroundings, including urban features (such as buildings and roads) and taller vegetation features (such as trees and hedges). This allows a more accurate estimation of local friction. The method produces a substantial node density due to the small dimensions of many urban features. Copyright © 2007 John Wiley & Sons, Ltd.     

Urban fluvial flood modelling using a two‐dimensional diffusion‐wave treatment,part 2: development of a sub‐grid‐scale treatment

This paper develops and tests a sub‐grid‐scale wetting and drying correction for use with two‐dimensional diffusion‐wave models of urban flood inundation. The method recognizes explicitly that representations of sub‐grid‐scale topography using roughness parameters will provide an inadequate representation of the effects of structural elements on the floodplain (e.g. buildings, walls), as such elements not only act as momentum sinks, but also have mass blockage effects. The latter may dominate, especially in structurally complex urban areas. The approach developed uses high‐resolution topographic data to develop explicit parameterization of sub‐grid‐scale topographic variability to represent both the volume of a grid cell that can be occupied by the flow and the effect of that variability upon the timing and direction of the lateral fluxes. This approach is found to give significantly better prediction of fluvial flood inundation in urban areas than traditional calibration of sub‐grid‐scale effects using Manning's n. In particular, it simultaneously reduces the need to use exceptionally high values of n to represent the effects of using a coarser mesh process representation and increases the sensitivity of model predictions to variation in n. Copyright © 2005 John Wiley & Sons, Ltd.     

Urban flood modelling combining top-view LiDAR data with ground-view SfM observations

Remote Sensing technologies are capable of providing high-resolution spatial data needed to set up advanced flood simulation models. Amongst them, aerial Light Detection and Ranging (LiDAR) surveys or Airborne Laser Scanner (ALS) systems have long been used to provide digital topographic maps. Nowadays, Remote Sensing data are commonly used to create Digital Terrain Models (DTMs) for detailed urban-flood modelling. However, the difficulty of relying on top-view LiDAR data only is that it cannot detect whether passages for floodwaters are hidden underneath vegetated areas or beneath overarching structures such as roads, railroads, and bridges. Such (hidden) small urban features can play an important role in urban flood propagation. In this paper, a complex urban area of Kuala Lumpur, Malaysia was chosen as a study area to simulate the extreme flooding event that occurred in 2003. Three different DTMs were generated and used as input for a two-dimensional (2D) urban flood model. A top-view LiDAR approach was used to create two DTMs: (i) a standard LiDAR-DTM and (ii) a Filtered LiDAR-DTM taking into account specific ground-view features. In addition, a Structure from Motion (SfM) approach was used to detect hidden urban features from a sequence of ground-view images; these ground-view SfM data were then combined with top-view Filtered LiDAR data to create (iii) a novel Multidimensional Fusion of Views-Digital Terrain Model (MFV-DTM). These DTMs were then used as a basis for the 2D urban flood model. The resulting dynamic flood maps are compared with observations at six measurement locations. It was found that when applying only top-view DTMs as input data, the flood simulation results appear to have mismatches in both floodwater depths and flood propagation patterns. In contrast, when employing the top-ground-view fusion approach (MFV-DTM), the results not only show a good agreement in floodwater depth, but also simulate more correctly the floodwater dynamics around small urban feature. Overall, the new multi-view approach of combining top-view LiDAR data with ground-view SfM observations shows a good potential for creating an accurate digital terrain map which can be then used as an input for a numerical urban flood model.     

Inundation extent as a key parameter for assessing the magnitude and return period of flooding events in southern Iceland

Abstract

River flow conditions in many watersheds of Iceland are particularly disturbed during winter by the formation, drifting and accumulation of river ice, whose impact on water encroachment and extent of inundations is not reflected in the discharge records. It is therefore necessary to use river discharge with great caution when assessing the magnitude of past inundations in Iceland, and to give attention to other flood magnitude parameters. A GIS-based methodology is presented that focuses on inundation extent as an alternative parameter for the assessment and ranking of the magnitude of past flooding events in the Ölfusá-Hvítá basin, known as one of the most dangerous flood-prone river complexes in Iceland. Relying ultimately on a macro-scale grid, the method enabled the reconstruction of the extent of inundations, the delineation of the flood plain, and, finally, some estimation of the likelihood of flooding of exposed areas that include marine submergences and river floods for both open water and ice conditions.

Citation Pagneux, E., Gísladóttir, G. & Snorrason, Á. (2010) Inundation extent as a key parameter for assessing the magnitude and return period of flooding events in southern Iceland. Hydrol. Sci. J. 55(5), 704–716.     

The application of integrated urban inundation model in Republic of Korea

In urban areas with a high building density, features such as roads, buildings and river dykes significantly affect flow dynamics and flood propagation. This should therefore be accounted for in the model set‐up. While 2D hydraulic models of densely urban areas are at the forefront of current research into flood inundation mechanisms, these models are constrained by inadequate parameters of topography and insufficient data. In order to solve these problems, topographic information obtained from digital elevation model (DEM) is directly programmed into the urban inundation model for a densely urban area, without exchanging the input data. In this paper, the extraction of building area is described using a tight coupling approach within a GIS environment, and its influence on the extent of flood inundation with a high building density is estimated. Copyright © 2009 John Wiley & Sons, Ltd.     

Remote measurement of river morphology via fusion of LiDAR topography and spectrally based bathymetry

This study developed and evaluated a hybrid approach to remote measurement of river morphology that combines LiDAR topography with spectrally based bathymetry. Comparison of filtered LiDAR point clouds with surveyed cross‐sections indicated that subtle features on low‐relief floodplains were accurately resolved by LiDAR but that submerged areas could not be detected due to strong absorption of near‐infrared laser pulses by water. The reduced number of returns made the active channel evident in a LiDAR point density map. A second dataset suggested that pulse intensity also could be used to discriminate land from water via a threshold‐based masking procedure. Fusion of LiDAR and optical data required accurate co‐registration of images to the LiDAR, and we developed an object‐oriented procedure for achieving this alignment. Information on flow depths was derived by correlating pixel values with field measurements of depth. Highly turbid conditions dictated a positive relation between green band radiance and flow depth and contributed to under‐prediction of pool depths. Water surface elevations extracted from the LiDAR along the water's edge were used to produce a continuous water surface that preserved along‐channel variations in slope. Subtracting local flow depths from this surface yielded estimates of the bed elevation that were then combined with LiDAR topography for exposed areas to create a composite representation of the riverine terrain. The accuracy of this terrain model was assessed via comparison with detailed field surveys. A map of elevation residuals showed that the greatest errors were associated with underestimation of pool depths and failure to capture cross‐stream differences in water surface elevation. Nevertheless, fusion of LiDAR and passive optical image data provided an efficient means of characterizing river morphology that would not have been possible if either dataset had been used in isolation. Copyright © 2011 John Wiley & Sons, Ltd.     

ParBreZo: A parallel,unstructured grid,Godunov-type,shallow-water code for high-resolution flood inundation modeling at the regional scale

Topographic data are increasingly available at high resolutions (<10 m) over large spatial extents to support detailed flood inundation modeling and loss estimation analyses required for flood risk management. This paper describes ParBreZo, the parallel implementation of a two-dimensional, Godunov-type, shallow-water code, to address the computational demand of high-resolution flood modeling at the regional scale (10²–10⁴ km²). A systematic approach to unstructured grid partitioning (domain decomposition) is presented, and the Single Process Multiple Data (SPMD) paradigm of distributed-memory parallelism is implemented so the code can be executed on computer clusters with distributed memory, shared memory, or some combination of the two (now common with multi-core architectures). In a fully-wetted, load-balanced test problem, the code scales very well with a parallel efficiency of close to 100% on up to 512 processes (maximum tested). A weighted grid partitioning is used to partially address the load balancing challenge posed by partially wetted domains germane to flooding applications, where the flood extent varies over time, while the partitioning remains static. An urban dam-break flood test problem shows that weighted partitions achieve a parallel efficiency exceeding 70% using up to 48 processes. This corresponds to a 97% reduction in execution time so results are obtained in a matter of minutes, which is attractive for routine engineering analyses. A hurricane storm surge test problem shows that a 10 m resolution, 12 h inundation forecast for a 40 km length of coastline can be completed in under 2 h using 512 processors. Hence, if coupled to a hurricane forecast system capable of resolving storm surge, inundation forecasts could be made at 10 m resolution with at least a 10 h lead time.     

Urban fluvial flood modelling using a two‐dimensional diffusion‐wave treatment,part 1: mesh resolution effects

High‐resolution data obtained from airborne remote sensing is increasing opportunities for representation of small‐scale structural elements (e.g. walls, buildings) in complex floodplain systems using two‐dimensional (2D) models of flood inundation. At the same time, 2D inundation models have been developed and shown to provide good predictions of flood inundation extent, with respect to both full solution of the depth‐averaged Navier–Stokes equations and simplified diffusion‐wave models. However, these models have yet to be applied extensively to urban areas. This paper applies a 2D raster‐based diffusion‐wave model to determine patterns of fluvial flood inundation in urban areas using high‐resolution topographic data and explores the effects of spatial resolution upon estimated inundation extent and flow routing process. Model response shows that even relatively small changes in model resolution have considerable effects on the predicted inundation extent and the timing of flood inundation. Timing sensitivity would be expected, given the relatively poor representation of inertial processes in a diffusion‐wave model. Sensitivity to inundation extent is more surprising, but is associated with: (1) the smoothing effect of mesh coarsening upon input topographical data; (2) poorer representation of both cell blockage and surface routing processes as the mesh is coarsened, where the flow routing is especially complex; and (3) the effects of (1) and (2) upon water levels and velocities, which in turn determine which parts of the floodplain the flow can actually travel to. It is shown that the combined effects of wetting and roughness parameters can compensate in part for a coarser mesh resolution. However, the coarser the resolution, the poorer the ability to control the inundation process, as these parameters not only affect the speed, but also the direction of wetting. Thus, high‐resolution data will need to be coupled to a more sophisticated representation of the inundation process in order to obtain effective predictions of flood inundation extent. This is explored in a companion paper. Copyright © 2005 John Wiley & Sons, Ltd.     

Delineation of flooding risk hotspots based on digital elevation model,calculated and historical flooding extents: the case of Ouagadougou

Delineation of flood risk hotspots can be considered as one of the first steps in an integrated methodology for urban flood risk management and mitigation. This paper presents a step-by-step methodology in a GIS-based framework for identifying flooding risk hotspots for residential buildings. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. The novelty of this paper consists in the fact that the flood-prone areas (the TWI thresholds) are identified through a maximum likelihood method (MLE) based both on inundation profiles calculated for a specific return period (T_R), and on information about the extent of historical flooding in the area of interest. Furthermore, Bayesian parameter updating is employed in order to estimate the TWI threshold by employing the historical extent as prior information and the inundation map for calculating the likelihood function. For different statistics of the TWI threshold, the map of potentially flood-prone areas is overlaid with the map of residential urban morphology units in order to delineate the residential flooding risk urban hotspots. Overlaying the delineated urban hotspots with geo-spatial census datasets, the number of people affected by flooding is estimated. These kind of screening procedures are particularly useful for locations where there is a lack of detailed data or where it is difficult to perform accurate flood risk assessment. In fact, an application of the proposed procedure is demonstrated for the identification of urban flooding risk hotspots in the city of Ouagadougou, capital of Burkina Faso, a city for which the observed spatial extent of a major flood event in 2009 and a calculated inundation map for a return period of 300 years are both available.     

Storm surge simulation along the U.S. East and Gulf Coasts using a multi-scale numerical model approach

The effectiveness of simulating surge inundation using the Eulerian–Lagrangian circulation (ELCIRC) model over multi-scale unstructured grids was examined in this study. The large domain model grid encompasses the western North Atlantic Ocean, the Gulf of Mexico, and the Caribbean Sea to appropriately account for remote and resonance effects during hurricane events and simplify the specification of the open boundary condition. The U.S. East and Gulf Coasts were divided into 12 overlapping basins with fine-resolution (up to 30 × 30 m) grids to model overland surge flooding. These overlapping basins have different fine-resolution grids near the coastal region, but have an identical coarse-resolution grid in the offshore region within the large model domain. Thus, the storm surge prediction can be conducted without reducing computation efficiency by executing multiple model runs with local fine-resolution grids where potential hurricane landfalls may occur. The capability of the multi-scale approach was examined by simulating storm surge caused by Hurricanes Andrew (1992) and Isabel (2003) along the South Florida coast and in the Chesapeake Bay. Comparisons between simulated and observed results suggest that multi-scale models proficiently simulated storm surges in the Biscayne Bay and the Chesapeake Bay during two hurricanes. A series of sensitivity tests demonstrated that the simulation of surge flooding was improved when LiDAR topographic data and special bottom drag coefficient values for mangrove forests were employed. The tests also showed that appropriate representation of linear hydrologic features is important for computing surge inundation in an urban area.     

Unstructured mesh generation and landcover-based resistance for hydrodynamic modeling of urban flooding

Urban flood inundation modeling with a hydrodynamic flow solver is addressed in this paper, focusing on strategies to effectively integrate geospatial data for unstructured mesh generation, building representation and flow resistance parameterization. Data considered include Light Detection and Ranging (LiDAR) terrain height surveys, aerial imagery and vector datasets such as building footprint polygons. First, a unstructured mesh-generation technique we term the building-hole method (BH) is developed whereby building footprint data define interior domain boundaries or mesh holes. A wall boundary condition depicts the impact of buildings on flood hydrodynamics. BH provides an alternative to the more commonly used method of raising terrain heights where buildings coincide with the mesh. We term this the building-block method (BB). Application of BH and BB to a flooding site in Glasgow, Scotland identifies a number of tradeoffs to consider at resolutions ranging from 1 to 5 m. At fine resolution, BH is shown to be similarly accurate but execute faster than BB. And at coarse resolution, BH is shown to preserve the geometry of buildings and maintain better accuracy than BB, but requires a longer run time. Meshes that ignore buildings completely (no-building method or NB) also support surprisingly good flood inundation predictions at coarse resolution compared to BH and BB. NB also supports faster execution times than BH at coarse resolution because the latter uses localized refinements that mandate a greater number of computational cells. However, with mesh refinement, NB converges to a different (and presumably less-accurate) solution compared to BH and BB. Using the same test conditions, Hunter et al. [Hunter NM, Bates PD, Neelz S, Pender G, Villanueva I, Wright NG, Liang D, et al. Benchmarking 2D hydraulic models for urban flood simulations. ICE J Water Manage 2008;161(1):13–30] compared the performance of dynamic-wave and diffusive-wave models and reported that diffusive-wave models under-predicted the longitudinal penetration of the flood zone due to important inertial effects. Here, we find that a relatively coarse-mesh implementation of a dynamic-wave model suffers from the same drawback because of numerical diffusion. This shows that whether diffusion is achieved through the mathematics or numerics, the effect on flood extent is similar. Finally, several methods of distributing resistance parameters (e.g., Manning n) across the Glasgow site were evaluated including methods that utilize aerial imagery-based landcover classification data, MasterMap^® landcover classification data and LiDAR-based feature height data (e.g., height of shrubs or hedges). Results show that landcover data is more important than feature height data in this urban site, that shadows in aerial imagery can cause errors in landcover classification which degrade flood predictions, and that aerial imagery offers a more detailed mapping of trees and bushes than MasterMap^® which can locally impact depth predictions but has little impact on flood extent.     

High‐resolution interpretative geomorphological mapping of river valley environments using airborne LiDAR data

The availability of airborne LiDAR data provides a new opportunity to overcome some of the problems associated with traditional, field‐based, geomorphological mapping such as restrictions on access and constraints of time or cost. The combination of airborne LiDAR data and GIS technology facilitates the rapid production of geomorphological maps of floodplain environments; however, unfiltered LiDAR data, which include vegetation and buildings, are currently more suitable for geomorphological mapping than data that have been filtered to remove these features. Classification of LiDAR data according to elevation in a GIS enables the user to identify and delineate geomorphological features in a manner similar to field mapping, but it is necessary to use a range of classification intervals in order to map the various types of feature that occur within a single reach. Comparison of a LiDAR‐derived geomorphological map with an independently produced field geomorphological map showed a high degree of similarity between the results of the two methods, although ground‐truthing is essential in cases where a high degree of accuracy is required. Ground‐truthing of a LiDAR‐derived geomorphological map showed that around 80% of features mapped using both methods were identified from the LiDAR data, suggesting that the method is suitable for applications such as production of base maps for use in field mapping and selection of sites for detailed investigation. Copyright © 2007 John Wiley & Sons, Ltd.     

Evaluating the effect of scale in flood inundation modelling in urban environments

Cellular‐based approaches for flood inundation modelling have been extensively calibrated and evaluated for the prediction of flood flows on rural river reaches. However, there has only been limited application of these approaches to urban environments, where the need for flood management is greatest. Practical application of two‐dimensional (2D) flood inundation models is often limited by computation time and processing power on standard desktop PCs when attempting to resolve flows on the high‐resolution grids necessary to replicate urban features. Consequently, it is necessary to evaluate the effectiveness of coarse grids to represent flood flows through urban environments. To examine these effects, LISFLOOD‐FP, a 2D storage cell model, is applied to hypothetical flooding scenarios in Greenfields, Glasgow. Grid resampling techniques in GIS software packages are evaluated and a bilinear gridding technique appears to provide the most accurate and physically intuitive results. A gridding method maintaining sharp elevation changes at building interfaces and neighbouring land is presented and estimates of the discretization noise associated with the coarse resolution grids suggest little improvement over current gridding methods. The variation in model results from the friction sensitivity analysis suggests a non‐stationary response to Manning's n with changing model resolution. Model results suggests that a coarse resolution model for urban applications is limited by the representation of urban media in coarse model grids. Furthermore, critical length scales related to building dimensions and building separation distances exist in urban areas that determine maximum possible grid resolutions for hydraulic models of urban flooding. Copyright ©, 2008 John Wiley & Sons, Ltd.     

Tsunami simulations on several scales

The tsunami event generated by the great Sumatra–Andaman earthquake on 26 December 2004 was simulated with the recently developed model TsunAWI. The model is based on the finite element method, which allows for a very flexible discretization of the model domain. This is demonstrated by a triangulation of the whole Indian Ocean with a resolution of about 14 km in the deep ocean but a considerably higher resolution of about 500 m in the coastal area. A special focus is put on the Banda Aceh region in the Northern tip of Sumatra. This area was heavily hit by the tsunami and the highest resolution in this area is about 40 m in order to include inundation processes in the model simulation. We compare model results to tide gauge data from all around the Indian Ocean, to satellite altimetry, and field measurements of flow depth in selected locations of the Aceh region. Furthermore, we compare the model results of TsunAWI to the results of a nested grid model (TUNAMI-N3) with the same initial conditions and identical bathymetry and topography in the Aceh region. It turns out that TsunAWI gives accurate estimates of arrival times in distant locations and in the same mesh gives good inundation results when compared to field measurements and nested grid results.     

Optimal use of high‐resolution topographic data in flood inundation models

In this paper we explore the optimum assimilation of high‐resolution data into numerical models using the example of topographic data provision for flood inundation simulation. First, we explore problems with current assimilation methods in which numerical grids are generated independent of topography. These include possible loss of significant length scales of topographic information, poor representation of the original surface and data redundancy. These are resolved through the development of a processing chain consisting of: (i) assessment of significant length scales of variation in the input data sets; (ii) determination of significant points within the data set; (iii) translation of these into a conforming model discretization that preserves solution quality for a given numerical solver; and (iv) incorporation of otherwise redundant sub‐grid data into the model in a computationally efficient manner. This processing chain is used to develop an optimal finite element discretization for a 12 km reach of the River Stour in Dorset, UK, for which a high‐resolution topographic data set derived from airborne laser altimetry (LiDAR) was available. For this reach, three simulations of a 1 in 4 year flood event were conducted: a control simulation with a mesh developed independent of topography, a simulation with a topographically optimum mesh, and a further simulation with the topographically optimum mesh incorporating the sub‐grid topographic data within a correction algorithm for dynamic wetting and drying in fixed grid models. The topographically optimum model is shown to represent better the ‘raw’ topographic data set and that differences between this surface and the control are hydraulically significant. Incorporation of sub‐grid topographic data has a less marked impact than getting the explicit hydraulic calculation correct, but still leads to important differences in model behaviour. The paper highlights the need for better validation data capable of discriminating between these competing approaches and begins to indicate what the characteristics of such a data set should be. More generally, the techniques developed here should prove useful for any data set where the resolution exceeds that of the model in which it is to be used. Copyright © 2002 John Wiley & Sons, Ltd.     

Effect of input data in hydraulic modeling for flood warning systems

The main objective of this study was to quantify the error associated with input data, including various resolutions of elevation datasets and Manning’s roughness for travel time computation and floodplain mapping. This was accomplished on the test bed, the Grand River (Ohio, USA) using the HEC-RAS model. LiDAR data integrated with survey data provided conservative predictions, whereas coarser elevation datasets provided a positive difference in the travel time (11.03–15.01%) and inundation area (32.56–44.52%). The minimum differences in travel time and inundation area were 0.50–4.33% and 3.55–7.16%, respectively, when the result from LiDAR integrated with survey data was compared with a 10-m DEM integrated with survey data. The results suggest that a 10-m DEM in the channel and LiDAR data in the floodplain combined with survey data would be appropriate for a flood warning system. Additionally, Manning’s roughness of the channel section was found to be more sensitive than that of the floodplain. The decrease in inundation area was highest (8.97%) for the lower value of Manning’s roughness.     

Towards spatially distributed quantitative assessment of tsunami inundation models

This paper presents a framework and data for spatially distributed assessment of tsunami inundation models. Our associated validation test is based upon the 2004 Indian Ocean tsunami, which affords a uniquely large amount of observational data for events of this kind. Specifically, we use eyewitness accounts to assess onshore flow depths and speeds as well as a detailed inundation survey of Patong City, Thailand to compare modelled and observed inundation. Model predictions matched well the detailed inundation survey as well as altimetry data from the JASON satellite, eyewitness accounts of wave front arrival times and onshore flow speeds. Important buildings and other structures were incorporated into the underlying elevation model and are shown to have a large influence on inundation extent.     

Modelling rating curves using remotely sensed LiDAR data

Accurate stream discharge measurements are important for many hydrological studies. In remote locations, however, it is often difficult to obtain stream flow information because of the difficulty in making the discharge measurements necessary to define stage‐discharge relationships (rating curves). This study investigates the feasibility of defining rating curves by using a fluid mechanics‐based model constrained with topographic data from an airborne LiDAR scanning. The study was carried out for an 8m‐wide channel in the boreal landscape of northern Sweden. LiDAR data were used to define channel geometry above a low flow water surface along the 90‐m surveyed reach. The channel topography below the water surface was estimated using the simple assumption of a flat streambed. The roughness for the modelled reach was back calculated from a single measurment of discharge. The topographic and roughness information was then used to model a rating curve. To isolate the potential influence of the flat bed assumption, a ‘hybrid model’ rating curve was developed on the basis of data combined from the LiDAR scan and a detailed ground survey. Whereas this hybrid model rating curve was in agreement with the direct measurements of discharge, the LiDAR model rating curve was equally in agreement with the medium and high flow measurements based on confidence intervals calculated from the direct measurements. The discrepancy between the LiDAR model rating curve and the low flow measurements was likely due to reduced roughness associated with unresolved submerged bed topography. Scanning during periods of low flow can help minimize this deficiency. These results suggest that combined ground surveys and LiDAR scans or multifrequency LiDAR scans that see ‘below’ the water surface (bathymetric LiDAR) could be useful in generating data needed to run such a fluid mechanics‐based model. This opens a realm of possibility to remotely sense and monitor stream flows in channels in remote locations. Copyright © 2012 John Wiley & Sons, Ltd.