Assessing the effect of different bathymetric models on hydraulic simulation of rivers in data sparse regions

Abstract River bathymetry, which is vital for accurate hydraulic modeling, is not readily available at large scales because of the logistical difficulties in field surveys and uncertainties associated with remote sensing techniques. Several studies have explored the potential of conceptual models and interpolation algorithms to estimate bathymetry. These models have certain underlying assumptions that limit their accuracy and widespread implementation. This study aims to provide insights into the choice of bathymetric model for different hydraulic applications by analyzing the effect of different bathymetric models on hydraulic modeling under different geomorphologic and flow settings. This study does not aim to reinforce the importance of bathymetry, rather its objective is to identify the bathymetric properties that are critical to accurate estimation of different hydraulic outputs. The study accomplishes its objectives by implementing three different bathymetric models with varying cost and efficiency at four sites with diverse bathymetric characteristics. Five hydraulic outputs, namely wetted cross-sectional area, water surface elevation, inundation extents, velocity and shear are estimated at three characteristic low and high flows and the results from the four sites are grouped together to perform an unbiased and robust evaluation. The performance of these models is evaluated using the best available bathymetric representation generated from detailed field surveys as a benchmark. The results indicate that 1D flood modeling is somewhat insensitive to channel shape as long as the estimated shape reflects the channel storage accurately. Velocity and shear related applications should incorporate bathymetry that represent both the cross-sectional area and channel thalweg accurately. Reaches with low sinuosity (

[1]  David R. Maidment,et al.  Anisotropic considerations while interpolating river channel bathymetry , 2006 .

[2]  W. Wallender,et al.  Synthetic river valleys: Creating prescribed topography for form–process inquiry and river rehabilitation design , 2014 .

[3]  P. Bates,et al.  The effects of spatial resolution and dimensionality on modeling regional‐scale hydraulics in a multichannel river , 2017 .

[4]  J. Bailly,et al.  Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near‐infrared and green signals , 2010 .

[5]  Roland K. Price,et al.  An optimized routing model for flood forecasting , 2009 .

[6]  P. Bates,et al.  Efficient incorporation of channel cross-section geometry uncertainty into regional and global scale flood inundation models , 2015 .

[7]  Zhigang Pan,et al.  Performance Assessment of High Resolution Airborne Full Waveform LiDAR for Shallow River Bathymetry , 2015, Remote. Sens..

[8]  P. Bates,et al.  A subgrid channel model for simulating river hydraulics and floodplain inundation over large and data sparse areas , 2012 .

[9]  G. Wilkerson,et al.  Improved Bankfull Channel Geometry Prediction Using Two‐Year Return‐Period Discharge 1 , 2011 .

[10]  David M. Bjerklie,et al.  Estimating the bankfull velocity and discharge for rivers using remotely sensed river morphology information , 2007 .

[11]  Alfred J. Kalyanapu,et al.  Approach to Digital Elevation Model Correction by Improving Channel Conveyance , 2015 .

[12]  Carl J. Legleiter,et al.  Calibrating remotely sensed river bathymetry in the absence of field measurements: Flow REsistance Equation‐Based Imaging of River Depths (FREEBIRD) , 2015 .

[13]  Ioana Popescu,et al.  River cross-section extraction from the ASTER global DEM for flood modeling , 2012, Environ. Model. Softw..

[14]  C. Legleiter.  Remote measurement of river morphology via fusion of LiDAR topography and spectrally based bathymetry , 2012 .

[15]  Carl J. Legleiter,et al.  Mapping gravel bed river bathymetry from space , 2012 .

[16]  V. Merwade,et al.  Deterministic Approach to Identify Ordinary High Water Marks Using Hydrologic and Hydraulic Attributes , 2017 .

[17]  Chris E. Jordan,et al.  A methodological intercomparison of topographic survey techniques for characterizing wadeable streams and rivers , 2014 .

[18]  A. Ducharne,et al.  Impact of river bed morphology on discharge and water levels simulated by a 1D Saint–Venant hydraulic model at regional scale , 2013 .

[19]  V. Smakhtin.  Low flow hydrology: a review , 2001 .

[20]  Stefano Alvisi,et al.  Estimation of bathymetry (and discharge) in natural river cross-sections by using an entropy approach , 2015 .

[21]  Efi Foufoula-Georgiou,et al.  Generalized hydraulic geometry: Insights based on fluvial instability analysis and a physical model , 2004 .

[22]  P. Bates,et al.  Amazon flood wave hydraulics , 2009 .

[23]  F. Fiedler,et al.  Effect of transect location, transect spacing and interpolation methods on river bathymetry accuracy , 2016 .

[24]  V. Merwade,et al.  Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping , 2009 .

[25]  J. Bailly,et al.  Geostatistical estimations of bathymetric LiDAR errors on rivers , 2010 .

[26]  Petteri Alho,et al.  Comparison of empirical and theoretical remote sensing based bathymetry models in river environments , 2012 .

[27]  W. Marcus,et al.  Remote sensing of rivers: the emergence of a subdiscipline in the river sciences , 2010 .

[28]  S. Rood,et al.  Streamflow requirements for cottonwood seedling recruitment—An integrative model , 1998, Wetlands.

[29]  J. Stromberg,et al.  Importance of low-flow and high-flow characteristics to restoration of riparian vegetation along rivers in arid south-western United States , 2007 .

[30]  Cheng Wang,et al.  Constructing river stage-discharge rating curves using remotely sensed river cross-sectional inundation areas and river bathymetry , 2016 .

[31]  Daniele Tonina,et al.  Effects of bathymetric lidar errors on flow properties predicted with a multi‐dimensional hydraulic model , 2014 .

[32]  B. Robinson.  Regional bankfull-channel dimensions of non-urban wadeable streams in Indiana , 2013 .

[33]  Venkatesh Merwade,et al.  A Faster and Economical Approach to Floodplain Mapping Using Soil Information , 2015 .

[34]  Integrated Modeling of Surface-Subsurface Processes to Understand River-Floodplain Hydrodynamics in the Upper Wabash River Basin , 2017 .

[35]  L. R. Beard.  Statistical Analysis in Hydrology , 1943 .

[36]  Dave Nagel,et al.  Remote Sensing of Channels and Riparian Zones with a Narrow-Beam Aquatic-Terrestrial LIDAR , 2009, Remote. Sens..

[37]  Sebastiaan N. Jonkman,et al.  Brief communication: Loss of life due to Hurricane Harvey , 2018 .

[38]  A. Casas,et al.  The topographic data source of digital terrain models as a key element in the accuracy of hydraulic flood modelling , 2006 .

[39]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[40]  Carl J. Legleiter,et al.  Mapping River Bathymetry With a Small Footprint Green LiDAR: Applications and Challenges 1 , 2013 .

[41]  P. Kyriakidis,et al.  Effects of uncertain topographic input data on two‐dimensional flow modeling in a gravel‐bed river , 2011 .

[42]  S. Saksena.  Investigating the role of dem resolution and accuracy on flood inundation mapping , 2015 .

[43]  E. Penning-Rowsell,et al.  Flood risk assessments at different spatial scales , 2015, Mitigation and Adaptation Strategies for Global Change.

[44]  P. Hudson,et al.  The influence of floodplain geomorphology and hydrologic connectivity on alligator gar (Atractosteus spatula) habitat along the embanked floodplain of the Lower Mississippi River , 2017 .

[45]  J. Nelson,et al.  Evaluation of an Experimental LiDAR for Surveying a Shallow, Braided, Sand-Bedded River , 2007 .

[46]  W. Andrew Marcus,et al.  Remote sensing of stream depths with hydraulically assisted bathymetry (HAB) models , 2005 .

[47]  Manuel A. Aguilar,et al.  Modelling vertical error in LiDAR-derived digital elevation models , 2010 .

[48]  Renaud Hostache,et al.  A drifting GPS buoy for retrieving effective riverbed bathymetry , 2015 .

[49]  V. Pauwels,et al.  Effective Representation of River Geometry in Hydraulic Flood Forecast Models , 2018 .

[50]  Venkatesh Merwade,et al.  Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping , 2015 .

[51]  Venkatesh M. Merwade,et al.  GIS techniques for creating river terrain models for hydrodynamic modeling and flood inundation mapping , 2008, Environ. Model. Softw..

[52]  Alfonso Mejia,et al.  Evaluating the effects of parameterized cross section shapes and simplified routing with a coupled distributed hydrologic and hydraulic model , 2011 .

[53]  Jean-Stéphane Bailly,et al.  Very-high-resolution mapping of river-immersed topography by remote sensing , 2008 .

[54]  J. T. Conner,et al.  Effect of cross‐section interpolated bathymetry on 2D hydrodynamic model results in a large river , 2014 .