Evaluation of a New Observationally Based Channel Parameterization for the National Water Model

Accurate representation of channel properties is important for forecasting in hydrologic models, as it affects height, celerity, and attenuation of flood waves. Yet, considerable uncertainty in the parameterization of channel geometry and hydraulic roughness (Manning’s n) exists within the NOAA National Water Model (NWM), due largely to data scarcity: only ~2,800 out of the 2.7 million river reach segments in the NWM have measured channel properties. In this study, we seek to improve channel representativeness by updating channel geometry and roughness parameters using a large, previously unpublished hydraulic geometry (HyG) dataset of approximately 48,000 gages. We begin with a Sobol’ sensitivity analysis of channel geometry parameters for 12 small semi-natural basins across the continental U.S., which reveals an outsized sensitivity of simulated flow to Manning’s n relative to channel geometry parameters. We then develop and evaluate a set of regression-based regionalizations of channel parameters estimated using the HyG dataset. Finally, we compare the model output generated from updated channel parameter sets to observations and the current NWM v2.1 parameterization. We find that, while the NWM land surface model holds the most influence over flow given its control over total volume, the updated channel parameterization leads to improvements in simulated streamflow performance relative to observed flows, with a statistically significant mean R2 increase from 0.479 to 0.494 across approximately 7,400 gage locations. HyG-based channel geometry and roughness provide a substantial overall improvement in channel representation over the default parameterization, updating the previous set value for most reaches of Manning’s n = 0.060 to a new range between 0.006 and 0.537 (median 0.077). This research provides a more representative, observationally based channel parameter dataset for the NWM routing module, as well as new insight into the influence of the routing module within the overall modeling framework.

The Manning’s Roughness Coefficient Calibration Method to Improve Flood Hazard Analysis in the Absence of River Bathymetric Data: Application to the Urban Historical Zamora City Centre in Spain

The accurate estimation of flood risk depends on, among other factors, a correct delineation of the floodable area and its associated hydrodynamic parameters. This characterization becomes fundamental in the flood hazard analyses that are carried out in urban areas. To achieve this objective, it is necessary to have a correct characterization of the topography, both inside the riverbed (bathymetry) and outside it. Outside the riverbed, the LiDAR data led to an important improvement, but not so inside the riverbed. To overcome these deficiencies, different models with simplified bathymetry or modified inflow hydrographs were used. Here, we present a model that is based upon the calibration of the Manning’s n value inside the riverbed. The use of abnormally low Manning’s n values made it possible to reproduce both the extent of the flooded area and the flow depth value within it (outside the riverbed) in an acceptable manner. The reduction in the average error in the flow depth value from 50–75 cm (models without bathymetry and “natural” Manning’s n values) to only about 10 cm (models without bathymetry and “calibrated” Manning’s n values), was propagated towards a reduction in the estimation of direct flood damage, which fell from 25–30% to about 5%.

Flood-inundation modeling in an operational context: sensitivity to topographic resolution and Manning's n

Streamflow forecasts from operational hydrologic models can be converted into forecasts of flood-inundation extent using either physically based hydraulic models or simpler terrain-based approaches. Two factors that influence simulated flood-inundation extent are spatial resolution of topographic data and in-channel and overland-flow roughness characterized by the Manning's n parameter. Here, AutoRoute, a raster-based flood-inundation model, was used to simulate two recent flood events in Florida (a forested floodplain) and Texas (an urban floodplain) using two different topographic resolutions and a range of Manning's n values. The AutoRoute-simulated flood-inundation extents were evaluated using observed extents from remotely sensed imagery. For comparison, the same flood events were also simulated using a one-dimensional Hydrologic Engineering Center River Analysis System (HEC-RAS) model. Results indicated that model performance was much improved with higher topographic resolution for the forested floodplain site and that the urban site was more sensitive to Manning's n. For the three different rivers analyzed, the fit for HEC-RAS was 5–10% higher than that for AutoRoute. Despite being only slightly less accurate than HEC-RAS in its simulation of flood extent, AutoRoute was much simpler to set up and required less computational time to run.

Evaluation of Manning’s n Roughness Coefficient in Arid Environments by Using SAR Backscatter

The prediction of arid region flash floods (magnitude and frequency) is essential to ensure the safety of human life and infrastructures and is commonly based on hydrological models. Traditionally, catchment characteristics are extracted using point-based measurements. A considerable improvement of point-based observations is offered by remote sensing technologies, which enables the determination of continuous spatial hydrological parameters and variables, such as surface roughness, which significantly influence runoff velocity and depth. Hydrological models commonly express the surface roughness using Manning’s roughness coefficient (n) as a key variable. The objectives were thus to determine surface roughness by exploiting a new high spatial resolution spaceborne synthetic aperture radar (SAR) technology and to examine the correlation between radar backscatter and Manning’s roughness coefficient in an arid environment. A very strong correlation (R2 = 0.97) was found between the constellation of small satellites for Mediterranean basin observation (COSMO)-SkyMed SAR backscatter and surface roughness. The results of this research demonstrate the feasibility of using an X-band spaceborne sensor with high spatial resolution for the evaluation of surface roughness in flat arid environments. The innovative method proposed to evaluate Manning’s n roughness coefficient in arid environments with sparse vegetation cover using radar backscatter may lead to improvements in the performance of hydrological models.

Anticipate Manning’s Coefficient in Meandering Compound Channels

Estimating Manning’s roughness coefficient ( n ) is one of the essential factors in predicting the discharge in a stream. Present research work is focused on prediction of Manning’s n in meandering compound channels by using the Group Method of Data Handling Neural Network (GMDH-NN) approach. The width ratio ( α ) , relative depth ( β ) , sinuosity ( s ) , Channel bed slope ( S o ) , and meander belt width ratio ( ω ) are specified as input parameters for the development of the model. The performance of GMDH-NN is evaluated with two different machine learning techniques, namely the support vector regression (SVR) and multivariate adaptive regression spline (MARS) with various statistical measures. Results indicate that the proposed GMDH-NN model predicts the Manning’s n satisfactorily as compared to the MARS and SVR model. This GMDH-NN approach can be useful for practical implementation as the prediction of Manning’s coefficient and subsequently discharge through Manning’s equation in the compound meandering channels are found to be quite adequate.