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The local‐inertial approximations of the shallow water equations (SWEs) have been used for flood forecasting at larger spatial scales owing to the improved computational efficiency and similar accuracy compared to the full 2D SWEs. With the availability of high‐resolution elevation data, the complex terrain of urban areas with various small‐scale features is represented well. Even for a local‐inertial model, utilizing such high‐resolution elevation data in flood simulations of urbanized areas increases the computational cost. A subgrid‐based local‐inertial formulation that permits large numerical grid size for computations while preserving the within‐grid topography is proposed to circumvent this. The subgrid topography can be incorporated into the coarse numerical grid computations by estimating the hydraulic properties, namely, volume and face area, based on water surface elevation variations of the associated high‐resolution terrain. The pre‐stored hydraulic properties are then used to dynamically update the hydraulic variables during the execution of the local‐inertial model. Idealized and real‐world test cases were simulated to illustrate the advantages of the proposed model. The proposed subgrid model performs better in capturing flood depth around subgrid‐scale features such as streets, highways, minor canals, etc., than the simple grid‐averaged local‐inertial models of the same grid size. The proposed model is faster than the existing local‐inertial model (e.g., LISFLOOD‐FP) (∼21–34 times) and the full 2D model (e.g., HEC‐RAS 2D) (∼361–660 times) of similar accuracy in the slow‐rising flood applications. Thus, the subgrid local‐inertial model holds promise in real‐time flood inundation forecasting, resolving smaller urban features. Plain Language Summary: Quick and accurate flood forecasting is crucial in highly populated urban areas with several small terrain features like roads, streets, highways, canals, etc. The simulation of flood depth and extent can be made faster by using simplified equations or dividing the domain into larger grids. The equations will be solved over each grid, generating flood depth and flow. However, opting for a large grid will not capture smaller within‐grid terrain elements. Therefore, this paper proposes a novel subgrid‐based local‐inertial model that uses simplified equations and operates at a large grid while still representing the within‐grid terrain. The subgrid‐inertial model simulates flooding around the smaller features of urban areas with less time than finer grid full 2D models. Meanwhile, the existing local‐inertial models operating at large grids often fail to resolve urban terrain features. Thus, using limited computing facilities, the proposed subgrid‐inertial model is highly suited for real‐time riverine flood forecasting in urbanized floodplains. Key Points: A subgrid‐based local‐inertial formulation that makes computations at coarse grids while capturing the within‐grid terrain is proposedIt is faster than a full 2D model and can resolve complex urban terrain (streets, canals, roads) where existing coarse grid models failIt improves mass flux estimation of coarse grids and holds promise in real‐time forecasting of slow‐rising urban floods [ABSTRACT FROM AUTHOR]

A simplified numerical model for simulation of floodplain inundation resulting from naturally occurring floods in rivers is presented. Flow through the river is computed by solving the de Saint Venant equations with a one-dimensional (1D) finite volume approach. Spread of excess flood water spilling overbank from the river onto the floodplains is computed using a storage cell model discretized into an unstructured triangular grid. Flow exchange between the one-dimensional river cells and the adjacent floodplain cells or that between adjoining floodplain cells is represented by diffusive-wave approximated equation. A common problem related to the stability of such coupled models is discussed and a solution by way of linearization offered. The accuracy of the computed flow depths by the proposed model is estimated with respect to those predicted by a two-dimensional (2D) finite volume model on hypothetical river-floodplain domains. Finally, the predicted extent of inundation for a flood event on a stretch of River Severn, United Kingdom, by the model is compared to those of two proven two-dimensional flow simulation models and with observed imagery of the flood extents. DOI: 10.1061/(ASCE)HY.1943-7900.0000211 CE Database subject headings: Shallow water; Numerical models; Flood plains; Flood routing; United Kingdom. Author keywords: Shallow water; Numerical models; Flood plains; Flood routing.

A simple yet precise relation between the flux gradient and the bed slope source term is presented, which produces a net force within the cell with an inclined water surface, but ensures still water condition when there is no flow across the boundaries. The proposed method consists of calculating the pressure term based on the water depths at the cell vertices, which may be computed by a higher order scheme and the bed slope source term by a centered discretization technique. The methodology is demonstrated with a Godunov-type upwind finite volume formulation. The inviscid fluxes are calculated using Roe's approximate Riemann solver and a second-order spatial accuracy is obtained by implementing multidimensional gradient reconstruction and slope limiting techniques. The accuracy and applicability of the numerical model is verified with a couple of test problems and a real flow example of tidal water movement in a stretch of River Hooghly in India. DOI: 10.1061/(ASCE)0733-9429(2008)134:2(231) CE Database subject headings: Shallow water; Unsteady flow; Numerical models; Topography; Grid systems; Tidal currents; India.

The article discusses the development and implementation of a real-time flood forecasting and spatial decision support system for Chennai, India. The system aims to mitigate the extensive damage caused by floods in the region. It utilizes ensemble rainfall forecasts and integrates meteorological, hydrological, and hydraulic models to provide accurate flood predictions. The system has been piloted and operationalized for the Northeast Monsoon seasons of 2021, 2022, and 2023, and has shown promising results in terms of forecast accuracy. The system also includes modules for short-term weather forecasting, inflow forecasting to reservoirs, lake operational guidance, hydraulic modeling, real-time data acquisition, and web-based decision support. [Extracted from the article]