Calibrating 1D hydrodynamic river models in the absence of cross-section geometry using satellite observations of water surface elevation and river width

Hydrodynamic modeling has been increasingly used to simulate water surface elevation which is important for flood prediction and risk assessment. Scarcity and inaccessibility of in situ bathymetric information have hindered hydrodynamic model development at continental-to-global scales. Therefore, river cross-section geometry is commonly approximated by highly simplified generic shapes. Hydrodynamic river models require both bed geometry and roughness as input parameters. Simultaneous calibration of shape parameters and roughness is difficult, because often there are trade-offs between them. Instead of parameterizing cross-section geometry and hydraulic roughness separately, this study introduces a parameterization of 1D hydrodynamic models by combining cross-section geometry and roughness into one conveyance parameter. Flow area and conveyance are expressed as power laws of flow depth, and they are found to be linearly related in log–log space at reach scale. Data from a wide range of river systems show that the linearity approximation is globally applicable. Because the two are expressed as power laws of flow depth, no further assumptions about channel geometry are needed. Therefore, the hydraulic inversion approach allows for calibrating flow area and conveyance curves in the absence of direct observations of bathymetry and hydraulic roughness. The feasibility and performance of the hydraulic inversion workflow are illustrated using satellite observations of river width and water surface elevation in the Songhua river, China. Results show that this approach is able to reproduce water level dynamics with root-mean-square error values of 0.44 and 0.50 m at two gauging stations, which is comparable to that achieved using a standard calibration approach. In summary, this study puts forward an alternative method to parameterize and calibrate river models using satellite observations of river width and water surface elevation.

An investigation into remote sensing techniques and field observations for modeling of dynamic hydraulic roughness from riparian vegetation

Riparian vegetation provides many noteworthy functions in river and floodplain systems including its influence on hydrodynamic processes. Traditional methods for predicting hydrodynamic characteristics in the presence of vegetation involve the application of static roughness ( n) values, which neglect changes in roughness due to local flow characteristics. The objectives of this study were to: (1) implement numerical routines for simulating dynamic hydraulic roughness ( n) in a two-dimensional (2D) hydrodynamic model; (2) evaluate the performance of two dynamic roughness approaches; and (3) compare vegetation parameters and hydrodynamic model results based on field-based and remote sensing acquisition methods. A coupled vegetation-hydraulic solver was developed for a 2D hydraulics model using two dynamic approaches, which required vegetation parameters to calculate spatially distributed, dynamic roughness coefficients. Vegetation parameters were determined by field survey and using airborne LiDAR data. Water surface elevations modeled using conventional and the proposed dynamic approaches produced similar profiles. The method demonstrates the suitability in modeling the system where there is no calibration data. Substantial spatial variations in both n and hydraulic parameters were observed when comparing the static and dynamic approaches. Thus, the method proposed here is beneficial for describing the hydraulic conditions for the area having huge variation of vegetation. The proposed methods have the potential to improve our ability to simulate the spatial and temporal heterogeneity of vegetated floodplain surfaces with an approach that is more physically-based and reproducible than conventional “look up” approaches. However, additional research is needed to quantify model performance with respect to spatially distributed flow properties and parameterization of vegetation characteristics.

Modeling of streamflow in a 30-kilometer-long reach spanning 5 years using OpenFOAM 5.x

Developing accurate and efficient modeling techniques for streamflow at tens-kilometer spatial scale and multi-year temporal scale is critical for evaluating and predicting the impact of climate- and human-induced discharge variations on river hydrodynamics. However, achieving such a goal is challenging because of limited surveys of streambed hydraulic roughness, uncertain boundary condition specifications, and high computational costs. We demonstrate that accurate and efficient three-dimensional (3D) hydrodynamic modeling of natural rivers at 30-kilometer and 5-year scales is feasible using the following three techniques within OpenFOAM, an open source computational fluid dynamics platform: 1) generating a distributed hydraulic roughness field for the streambed by integrating water stage observation data, a rough wall theory, and a local roughness optimization and adjustment strategy; 2) prescribing the boundary condition for the inflow and outflow by integrating pre-computed results of a one-dimensional (1D) hydraulic model with the 3D model; and 3) reducing computational time using multiple parallel runs constrained by 1D inflow and outflow boundary conditions. Streamflow modeling for a 30-kilometer-long reach in the Columbia River (CR) over 58 months can be achieved in less than six days using 1.1 million CPU hours. The mean error between the modeled and the observed water stages for our simulated CR reach ranges from −16 cm to 9 cm (equivalent to ca. ±7 % relative to the average water depth) at seven locations during most of the years between 2011 and 2019. We can reproduce the velocity distribution measured by the acoustic Doppler current profiler (ADCP). The correlation coefficients of the depth-averaged velocity between the model and ADCP measurements are in the range between 0.71 and 0.83 at 75 % of the survey cross-sections. With the validated model, we further show that the relative importance of dynamic pressure versus hydrostatic pressure varies with discharge variations and topography heterogeneity. Given the model's high accuracy and computational efficiency, the model framework provides a generic approach to evaluate and predict the impact of climate- and human-induced discharge variations on river hydrodynamics at tens kilometer and decade scales.

Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry

Vegetation heights derived from drone laser scanning (DLS), and structure from motion (SfM) photogrammetry at the Virginia Tech StREAM Lab were utilized to determine hydraulic roughness (Manning’s roughness coefficients). We determined hydraulic roughness at three spatial scales: reach, patch, and pixel. For the reach scale, one roughness value was set for the channel, and one value for the entire floodplain. For the patch scale, vegetation heights were used to classify the floodplain into grass, scrub, and small and large trees, with a single roughness value for each. The roughness values for the reach and patch methods were calibrated using a two-dimensional (2D) hydrodynamic model (HEC-RAS) and data from in situ velocity sensors. For the pixel method, we applied empirical equations that directly estimated roughness from vegetation height for each pixel of the raster (no calibration necessary). Model simulations incorporating these roughness datasets in 2D HEC-RAS were validated against water surface elevations (WSE) from seventeen groundwater wells for seven high-flow events during the Fall of 2018 and 2019, and compared to marked flood extents. The reach method tended to overestimate while the pixel method tended to underestimate the flood extent. There were no visual differences between DLS and SfM within the pixel and patch methods when comparing flood extents. All model simulations were not significantly different with respect to the well WSEs (p > 0.05). The pixel methods had the lowest WSE RMSEs (SfM: 0.136 m, DLS: 0.124 m). The other methods had RMSE values 0.01–0.02 m larger than the DLS pixel method. Models with DLS data also had lower WSE RMSEs by 0.01 m when compared to models utilizing SfM. This difference might not justify the increased cost of a DLS setup over SfM (~150,000 vs. ~2000 USD for this study), though our use of the DLS DEM to determine SfM vegetation heights might explain this minimal difference. We expect a poorer performance of the SfM-derived vegetation heights/roughness values if we were using a SfM DEM, although further work is needed. These results will help improve hydrodynamic modeling efforts, which are becoming increasingly important for management and planning in response to climate change, specifically in regions were high flow events are increasing.

Calibrating 1D hydrodynamic river models in the absence of cross-sectional geometry: A new parameterization scheme

Hydrodynamic modeling has been increasingly used to simulate water surface elevation which is important for flood prediction and risk assessment. Scarcity/inaccessibility of in-situ bathymetric information has hindered hydrodynamic model development at continental-global scales. Therefore, river cross-section geometry has commonly been approximated using highly simplified generic shapes. However, strong correlations appear between cross-section shape parameters and hydraulic roughness in a hydraulic inversion approach. This study introduces a novel parameterization of 1D hydrodynamic models that reduces ambiguity by combining cross-section geometry and roughness into a conveyance parameter. Flow area and conveyance are expressed as power-law functions of flow depth, and thus are assumed to be linearly related in log-log space at reach scale. Data from a wide range of river systems show that the linearity approximation is globally applicable. Because the two are expressed as power-law functions of flow depth, no further assumptions about channel geometry are needed. Therefore, the hydraulic inversion approach allows for calibrating flow area and conveyance curves in the absence of bathymetry and hydraulic roughness. Its feasibility and performance are illustrated using satellite observations of river width and water surface elevation.

Quantifying hydraulic roughness from field data: can bed morphology tell the whole story?

<p>Bedforms are thought to be a major cause of hydraulic roughness in channels. The geometry of the river bed, shaped by bars, dunes, and ripples, and the spatial and temporal distribution of these, influence the resulting roughness variations. Roughness is a fundamental parameter for understanding river flow behaviour by influencing sediment transport and water level.</p><p>Quantification of roughness is challenging since it is not directly measurable in the field. It is therefore inferred from hydrological characteristics, -including water depth, water surface slope, flow velocity, discharge-, as well as morphological characteristics, -such as bedform height-, or derived from calibration of a hydraulic model.</p><p>This study contributes to the elucidation of factors influencing hydraulic roughness, and its quantification from field data. Proper quantification of roughness and its spatiotemporal behavior will increase our knowledge in river behavior and will lead to improvement of river management strategies and operational models.</p><p>In this research, three methods will be explored, to quantify the spatial distribution of hydraulic roughness in the field. We aim to state the importance of bed morphology for hydraulic roughness and we pursue the auxiliary aim to explore the spatial distribution of bedforms and roughness in our case study area river Waal, the Netherlands.</p><p>Method 1 uses the St. Vernant equations (better known as the Chezy equations) to quantify roughness, with as input among others flow velocity, bed slope and water surface slope. This value is seen as the ‘true’  roughness of the river system. Method 2 is a traditionally often used method, where form roughness is obtained from dune characteristics such as height and length via empirical predictors. Method 3 makes use of characteristics of the bed itself, not strictly related to 2D bedform geometry, specifically the inclination of the streamwise local elevation profile, i.e. local topographic leeside angle. Doing so eliminates the necessity of defining dune characteristics, and therefore taking one, often arbitrary, step out of the procedure to quantify roughness.</p><p>The three methodologies show the same general trend and order of magnitude of roughness (C=30-70 m<sup>0.5</sup>/s, mean 42 m<sup>0.5</sup>/s) however kilometer-scale variations show contrasting patterns. Nor dune geometry neither local topographic leeside angle manage to fully explain the variations in the roughness as obtain from the st. Vernant equations. From this we conclude that bed morphology does not seem to be the only explaining factor for roughness variations. Possible explanations include the low leeside angle of dunes (mean <10°), the influence of man-made structures such as groynes and longitudinal training dams, the influence of fixed gravel layers in sharp bends, river curvature, and cross-sectional variation in river depth (bars) and flow velocity. Further steps will be made to unravel the contributing factors for spatial variation in roughness.</p>

Hydraulic roughness estimation in vegetated floodplains

<p>The continuous interaction between riparian vegetation and water has important effects on the hydraulics of a river, mainly onto the flood events propagation. Vegetation is a fundamental part of the river ecosystem, but its stage and growth need to be monitored and controlled, especially when the river passes through a densely urbanized area. In fact, vegetation obstructs the streamflow by reducing the hydraulic cross-section area and increasing the roughness of the floodplains and the relative flood risk.</p><p>In this study, experiments have been performed at the Fantoli Hydraulic Laboratory at Politecnico di Milano, to validate the methodologies that estimate the hydraulic roughness of vegetated river floodplains, starting from the vegetation properties such as size, density and elastic modulus of a case study. A model based on the mechanical properties of vegetation was used to identify the most suitable material to reproduce the dynamic behaviour of real vegetation on a laboratory scale. The tests were carried out for different spatial configurations of trees, densities and submerged conditions.</p><p>The analysis, in addition to relying on experimental work, involves the installation of six piezoresistive pressure sensors located both in the floodplains and in the main channel, to monitor head losses in a representative reach of the river under study. The field measurements allow validation of the approach used in laboratory tests.</p>

WET-EcoServices Version 2: A revised ecosystem services assessment technique, and its application to selected wetland and riparian areas

A rapid assessment technique, termed WET-EcoServices, was developed 10 years ago to help assess the ecosystem services that individual wetland hydrogeomorphic units supply. The technique requires the assessor to consider and score a suite of indicators (e.g., hydraulic roughness of the vegetation) which are then used to rate the ability of the wetland to provide 16 different ecosystem services. WET-EcoServices has become well entrenched in the South African context, with wetland specialists routinely using the technique to inform development planning, whilst it has also been used extensively in the wetland rehabilitation context. The technique has recently been revised, including the following key changes: (i) the technique is now more explicit in terms of distinguishing both ecosystem services’ supply and the demand for all ecosystem services assessed; (ii) the technique has been expanded to include non-wetland riparian areas; (iii) several of the indicators have been refined or replaced with indicators more relevant or appropriate for informing the rating of the ecosystem service or for which information is more readily available at a national level; and (iv) the algorithms used to integrate scores for the relevant indicators have been comprehensively refined so as to better account for the relative importance of the respective indicators. The aim of this paper is to present an overview of Version 2 of the technique and its underlying approach and then to demonstrate its application to 6 selected cases representing contrasting contexts, with a particular focus on the graphical representation of ecosystem service supply and demand for each case. Some of the key emphases and approaches applied by WET-EcoServices are then discussed in relation to other published techniques widely used for assessing wetland ecosystem services. After reflecting on some key limitations of WET-EcoServices, the paper concludes with recommendations on the technique’s potential contributions to operationalizing key broad imperatives of government.