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

Abstract

Abstract:A methodology is presented which can be used in the evaluation of parametric uncertainty in urban flooding simulation. Due to the fact that such simulations are time consuming, the following methodology is proposed: (a) simplification of the description of the physical process; (b) derivation of a training data set; (c) development of a data-driven surrogate model; (d) use of a forward uncertainty propagation scheme. The simplification comprises the following steps: (a) unit hydrograph derivation using a 2D hydrodynamic model; (b) calculation of the losses in order to determine the effective rainfall depth; (c) flood event simulation using the principle of the proportionality and superposition. The above methodology was implemented in an urban catchment located in the city of Athens, Greece. The model used for the first step of the simplification was FLOW-R2D, whereas the well-known SWMM software (US Environmental Protection Agency, Washington, DC, USA) was used for the second step of the simplification. For the training data set derivation, an ensemble of 100 Unit Hydrographs was derived with the FLOW-R2D model. The parameters which were modified in order to produce this ensemble were the Manning coefficients in the two friction zones (residential and urban open space areas). The surrogate model used to replicate the unit hydrograph derivation, using the Manning coefficients as an input, was based on the Polynomial Chaos Expansion technique. It was found that, although the uncertainties in the derived results have to be taken into account, the proposed methodology can be a fast and efficient way to cope with dynamic flood simulation in an urban catchment.

Simulation of Low Impact Development (LID) Practices and Comparison with Conventional Drainage Solutions

“…The software used in modelling conventional measures and LID practices was SWMM5.1 of the U.S. Environmental Protection Agency. SWMM is a fully dynamic rainfall-runoff model used for the simulation of water quantity, quality and LID controls in urban areas [22][23][24][25][26][27]. Infiltration computations for the entire study area were based on the Curve Number method.…”

“…In hydraulic calculations, the Dynamic Wave model was used with time step fixed at 0.1 s. SWMM software parameters and their variation ranges are presented in Table 1. Subcatchment information, such as area, slope, percent imperviousness and curve number values, were estimated based on the Digital Elevation Model (DEM) and the land uses of the study area using GIS techniques, while typical values from the literature were used for [26,[28][29][30]: (i) width; (ii) Manning's roughness coefficient for overland flow in pervious and impervious surfaces; (iii) depression storage for pervious and imperious areas; and (iv) Manning's roughness coefficient for storm sewers. As the study site is ungauged, the following IDF curves were used in simulating existing condition, conventional measures and LID practices [31]:…”

Metamodel-assisted analysis of an integrated model composition: An example using linked surface water – groundwater models

“…Recently, another family of metamodels, the Polynomial Chaos Expansions (PCEs) (Xiu and Karniadakis, 2002), have been applied in environmental modelling to address uncertainty quantification and sensitivity analysis problems (e.g. Rajabi et al, 2015 and references therein;Babaei et al, 2015;Bellos et al, 2017). Another approach, less common within environmental modelling, is the use of physics-based metamodels which are constructed by combining models of different fidelity (e.g.…”

Flood prediction in a compound channel using machine learning techniques

“…One way for achieving results of higher accuracy with low computational cost, is the use of machine learning techniques (Carbajal et al, 2017;Bellos et al 2017). In this study, we present an example of using an emulator for a detailed 2D hydrodynamic simulator, named FLOW-R2D (Tsakiris and Bellos, 2014), in a hypothetical case study.…”