Quantifying Roughness Coefficient Uncertainty in Urban Flooding Simulations through a Simplified Methodology

Acclimatize experimental approach to adjudicate hydraulic coefficients under different bed material configurations and slopes with and without weir

The primary purpose of this study was to evaluate the hydraulic coefficient of coarse aggregate grain size beds and hydraulic parameters under random and perpendicular bed configurations, as well as to explore the discharge coefficient for rectangular weirs. The research objectives were to compare flow resistance coefficients, evaluate the discharge coefficient for rectangular weirs, investigate the relationship between roughness coefficient and hydraulic parameters, and validate the theoretical hydraulic equation for the rectangular weir. This was achieved by analysing different bed configurations, bed slopes, and 20 and 30-mm bed materials. Sieve analysis was conducted on bed materials using American-standard sieves to determine their particle size distribution. The experiment was performed in a rectangular flume measuring 12 m in length, 0.31 m in width, and 0.45 m in depth. In a laboratory experiment, water was pumped into a flume using centrifugal pumps, and a rectangular weir was attached downstream for discharge measurement. The experiment investigated factors such as Manning roughness coefficient, bed material geometry, bed slope, and weir shapes. Approximately 1680 tests were conducted to analysed the impact of these factors on discharge and the coefficient of discharge. The average Manning's roughness coefficients for a grain size of 20 mm were 0.019 (with weir) and 0.019 (without weir) in a random bed configuration, and 0.028 (with weir) and 0.027 (without weir) in a perpendicular flow bed configuration. For a grain size of 30 mm, the coefficients were 0.023 (with weir) and 0.022 (without weir) in a random bed configuration, and 0.033 (with weir) and 0.026 (without weir) in a perpendicular flow bed configuration. The presence of a weir has affected Manning's roughness coefficients and discharge coefficients. With a weir, the roughness coefficients have generally been higher compared to without a weir, indicating increased roughness in the channel. The discharge coefficient for a rectangular weir with a grain size of 20 mm ranged from 0.39 to 0.84 (random bed) and 0.27 to 0.68 (perpendicular flow bed), while for a grain size of 30 mm it ranged from 0.31 to 0.81 (random bed) and 0.23 to 0.48 (perpendicular flow bed). The discharge coefficients have varied depending on the grain size and bed configuration, reflecting different flow efficiencies over the weir. Rough particles influenced flow and Manning's roughness coefficient value, then reduced discharge and velocity values. Under two bed configurations and slopes, beds with a grain size of 30 mm have higher roughness coefficients compared to those with a grain size of 20 mm. The models have shown that the roughness coefficient is inversely proportional to the discharge and directly proportional to the tailgate water levels. The coefficient of roughness and discharge coefficient are mainly influenced by the channel slopes, bed roughness, bed grain size, and bed configuration. A randomly configured bed with a 20 mm grain size gravel bed is preferred over a perpendicular bed configuration to handle high discharges. Using a 20 mm grain-size gravel bed in open-channel flow is more suitable than a 30 mm grain-size bed. Relying on the constant friction factor, Manning's n, is not recommended as it may result in design errors. These findings have the potential to improve hydraulic engineering design practices, enhancing the accuracy and efficiency of open-channel flow systems.

Modeling rainfall-induced 2D inundation simulation based on the ANN-derived models with precipitation and water-level measurements at roadside IoT sensors

This study aims to develop a smart model for carrying out two-dimensional (2D) inundation simulation by estimating the gridded inundation depths via the ANN-derived models (ANN_GA-SA_MTF), named SM_EID_2D model. Within the SM_EID_2D model, the rainfall-induced inundation depths at the IoT sensors (i.e., IOT-based grids) are first estimated to be then used in the estimation of inundation depths at the ungauged grids (VIOT-based grids), the resulting flood extents and spatial distribution of inundation of what could be achieved. To facilitate the reliability of the proposed SM_EID_2D model in the 2D inundation simulation, a considerable number of rainfall-induced flood events are generated as the training datasets by coupling the hydrodynamic numerical model (SOBEK) with the simulated gridded rainstorms. To proceed with the model validation and application, the Miaoli City of North Taiwan is selected as the study area, and the associated hydrological and geographical data are adopted in the generation of the training datasets. The results from the model validation indicate that the proposed SM_EID_2D model could provide the gridded inundation-depth hydrographs with a low bias (about 0.02 m) and a high fitness to the validated data (nearly 0.7); also, the spatial distribution of inundated and non-inundated grids as well as the induced flooding extent provided could be well emulated by the proposed SM_EID_2D model under acceptable reliability (0.7). The proposed SM_EID_2D model is also advantageous for the 2D inundation simulation in the real-time delineated subbasins by assembling the emulated inundation depths at the specific grids.

Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling

Natural disasters, including droughts and floods, have caused huge losses to mankind. Hydrological modelling is an indispensable tool for obtaining a better understanding of hydrological processes. The DEM-based routing methods, which are widely used in the distributed hydrological models, are sensitive to both the DEM resolution and the computational cell size. Too little work has been devoted to the joint effects of DEM resolution and computational cell size on the routing methods. This study aims to study the joint effects of those two factors on discharge simulation performance with two representative routing methods. The selected methods are the improved aggregated network-response function routing method (I-NRF) and the Liner-reservoir-routing method (LRR). Those two routing methods are combined with two runoff generation models to simulate the discharge. The discharge simulation performance is evaluated under the cross combination of four DEM resolutions (i.e., 90 m, 250 m, 500 m, and 1000 m) and fifty-six computational cell sizes (ranging from 5 arc-min to 60 arc-min). Eleven years of hydroclimatic data from the Jianxi basin (2000–2010) and the Shizhenjie basin (1983–1993) in China are used. The results show that the effects of the DEM resolution and the computational cell size are different on the I-NRF method and the LRR method. The computational cell size has nearly no influence on the performance of the I-NRF methods, while the DEM resolution does. On the contrary, the LRR discharge simulation performance decreases with oscillating values as the computational cell size increases, but is hardly affected by the DEM resolution. Furthermore, the joint effects of the DEM resolution and the computational cell size can be ignored for both routing methods. The results of this study will help to establish the appropriate DEM resolution, computational cell size, and routing method when researchers build hydrological models to predict future disasters.

Two-Dimensional Flood Inundation Modeling in the Godavari River Basin, India—Insights on Model Output Uncertainty

Most flood inundation models do not come with an uncertainty analysis component chiefly because of the complexity associated with model calibration. Additionally, the fact that the models are both data- and compute-intensive, and since uncertainty results from multiple sources, adds another layer of complexity for model use. In the present study, flood inundation modeling was performed in the Godavari River Basin using the Hydrologic Engineering Center—River Analysis System 2D (HEC-RAS 2D) model. The model simulations were generated for six different scenarios that resulted from combinations of different geometric, hydraulic and hydrologic conditions. Thus, the resulted simulations account for multiple sources of uncertainty. The SRTM-30 m and MERIT-90 m Digital elevation Model (DEM), two sets of Manning’s roughness coefficient (Manning’s n) and observed and estimated boundary conditions, were used to reflect geometric, hydraulic and hydrologic uncertainties, respectively. The HEC-RAS 2D model ran in an unsteady state mode for the abovementioned six scenarios for the selected three flood events that were observed in three different years, i.e., 1986, 2005 and 2015. The water surface elevation (H) was compared in all scenarios as well as with the observed values at selected locations. In addition, ‘H’ values were analyzed for two different structures of the computational model. The average correlation coefficient (r) between the observed and simulated H values is greater than 0.85, and the highest r, i.e., 0.95, was observed for the combination of MERIT-90 m DEM and optimized (obtained via trial and error) Manning’s n. The analysis shows uncertainty in the river geometry information, and the results highlight the varying role of geometric, hydraulic and hydrologic conditions in the water surface elevation estimates. In addition to the role of the abovementioned, the study recommends a systematic model calibration and river junction modeling to understand the hydrodynamics upstream and downstream of the junction.

Propagating Dam Breach Parametric Uncertainty in a River Reach Using the HEC-RAS Software

Dam break studies consist of two submodels: (a) the dam breach submodel which derives the flood hydrograph and (b) the hydrodynamic submodel which, using the flood hydrograph, derives the flood peaks and maximum water depths in the downstream reaches of the river. In this paper, a thorough investigation of the uncertainty observed in the output of the hydrodynamic model, due to the seven dam breach parameters, is performed in a real-world case study (Papadiana Dam, located at Tavronitis River in Crete, Greece). Three levels of uncertainty are examined (flow peak of the flood hydrograph at the dam location, flow peaks and maximum water depths downstream along the river) with two methods: (a) a Morris-based sensitivity analysis for investigating the influence of each parameter on the final results; (b) a Monte Carlo-based forward uncertainty analysis for defining the distribution of uncertainty band and its statistical characteristics. Among others, it is found that uncertainty of the flow peaks is greater than the uncertainty of the maximum water depths, whereas there is a decreasing trend of uncertainty as we move downstream along the river.

A robust approach for comparing conventional and sustainable flood mitigation measures in urban basins

An integrated methodological framework for assessing different flood mitigation measures in urban catchments is presented. The framework comprises hydrologic, hydraulic and economic indices aiming at quantifying the effect of different alternatives regarding flood hazard mitigation. The alternatives evaluated include both conventional drainage solutions and low impact development measures. The conventional drainage solutions were: (i) off-line detention tanks; and (ii) sewer enlargement. The low impact development measures included: (i) green roofs (GR); and (ii) permeable surfaces (PS). Each solution was modeled using SWMM5 with respect to flood reduction effectiveness, and the results were compared to those of the existing condition (i.e., no flood mitigation measures). All the examined solutions were also compared based on their construction and operation and maintenance costs for a typical lifespan (i.e., 30 years). The results of the simulation revealed that both low impact development measures and conventional drainage solutions were highly effective even for storm events with low probability of occurrence. However, sewer enlargement was found to be the best alternative from an economic perspective. Nevertheless, peak at the sewer exit increased and time to peak remained unchanged; as a result, local flooding problems are resolved but downstream flooding problems may be introduced. If other criteria are considered, i.e., traffic obstruction, noise, construction easiness, co-benefits and downstream impacts, low impact development measures become more attractive compared to conventional drainage solutions.

Recent insights on uncertainties present in integrated catchment water quality modelling

This paper aims to stimulate discussion based on the experiences derived from the QUICS project (Quantifying Uncertainty in Integrated Catchment Studies). First it briefly discusses the current state of knowledge on uncertainties in sub-models of integrated catchment models and the existing frameworks for analysing uncertainty. Furthermore, it compares the relative approaches of both building and calibrating fully integrated models or linking separate sub-models. It also discusses the implications of model linkage on overall uncertainty and how to define an acceptable level of model complexity. This discussion includes, whether we should shift our attention from uncertainties due to linkage, when using linked models, to uncertainties in model structure by necessary simplification or by using more parameters. This discussion attempts to address the question as to whether there is an increase in uncertainty by linking these models or if a compensation effect could take place and that overall uncertainty in key water quality parameters actually decreases. Finally, challenges in the application of uncertainty analysis in integrated catchment water quality modelling, as encountered in this project, are discussed and recommendations for future research areas are highlighted.

A dynamic emulator for physically based flow simulators under varying rainfall and parametric conditions

This work presents a method to emulate the flow dynamics of physically based hydrodynamic simulators under variations of time-dependent rainfall and parametric scenarios. Although surrogate modelling is often employed to deal with the computational burden of this type of simulators, common techniques used for model emulation as polynomial expansions or Gaussian processes cannot deal with large parameter space dimensionality. This restricts their applicability to a reduced number of static parameters under a fixed rainfall process. The technique presented combines the use of a modified Unit Hydrograph (UH) scheme and a polynomial chaos expansion (PCE) to emulate flow from physically based hydrodynamic models. The novel element of the proposed methodology is that the emulator compensates for the errors induced by the assumptions of proportionality and superposition of the UH theory when dealing with non-linear model structures, whereas it approximates properly the behaviour of a physically based simulator to new (spatially-uniform) rainfall time-series and parametric scenarios. The computational time is significantly reduced, which makes the practical use of the model feasible (e.g. real time control, flood warning schemes, hydraulic structures design, parametric inference etc.). The applicability of this methodology is demonstrated in three case studies, through the emulation of a simplified non-linear tank-in-series routing structure and of the 2D Shallow Water Equations (2D-SWE) solution (FLOW-R2D) in two computational domains. Results indicate that the proposed emulator can approximate with a high degree of accuracy the behaviour of the original models under a wide range of rainfall inputs and parametric values.