Solving river engineering problems typically requires river flow characterization, including the prediction of flow depth, flow velocity, and flood extent. Hydraulic models use governing equations of the flow in motion (conservation of mass and momentum principles) to predict the flow characteristics. However, solving such equations can be substantially expensive, depending upon their spatial extension. Moreover, modeling two- or three-dimensional river flows with high-resolution topographic data for large-scale regions (national or continental scale) is next to impossible. Such simulations are required for comprehensive river modeling, where a system of connected rivers is to be simulated simultaneously. Machine Learning (ML) approaches have shown promise for different water resources problems, and they have demonstrated an ability to learn from current data to predict new scenarios, which can enhance the understanding of the systems. The aim of this paper is to present an efficient flood simulation framework that can be applied to large-scale simulations. The framework outlines a novel, quick, efficient and versatile model to identify flooded areas and the flood depth, using a hybrid of hydraulic model and ML measures. To accomplish that, a two-dimensional hydraulic model (iRIC), calibrated by measured water surface elevation data, was used to train two ML models to predict river depth over the domain for an arbitrary discharge. The first ML model included a random forest (RF) classification model, which was used to identify wet or dry nodes over the domain. The second was a multilayer perceptron (MLP) model that was developed and trained by the iRIC simulation results, in order to estimate river depth in wet nodes. For the test data the overall accuracy of 98.5 percent was achieved for the RF classification. The regression coefficient for the MLP model for depth was 0.88. The framework outlined in this paper can be used to couple hydraulics and ML models to reduce the computation time, resources and expenses of large-scale, real-time simulations, specifically for two- or three-dimensional hydraulic modeling, where traditional hydraulic models are infeasible or prohibitively expensive.

While deterministic forecasts provide a single realization of potential inundation, the inherent uncertainty associated with forecasts also needs to be conveyed for improved decision support. The objective of this study was to develop an ensemble framework for the quantification and visualization of uncertainty associated with flood inundation forecast maps. An 11‐member ensemble streamflow forecast at lead times from 0 to 48 hr was used to force two hydraulic models to produce a multimodel ensemble. The hydraulic models used are (1) the International River Interface Cooperative along with Flow and Sediment Transport with Morphological Evolution of Channels solver and (2) the two‐dimensional Hydrologic Engineering Center‐River Analysis System. Uncertainty was quantified and augmented onto flood inundation maps by calculating statistical spread among the ensemble members. For visualization, a series of probability flood maps conveying the uncertainty in forecasted water extent, water depth, and flow velocity was disseminated through a web‐based decision support tool. The results from this study offer a framework for quantifying and visualizing model uncertainty in forecasted flood inundation maps.

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Predicting morphological alterations in backwater zones has substantial merit as it potentially influences the life of millions of people by the change in flood dynamics and land topography. While there is no two-dimensional river model available for predicting morphological alterations in backwater zones, there is an absolute need for such models. This study presents an integrated iterative two-dimensional fluvial morphological model to quantify spatio-temporal fluvial morphological alterations in normal flow to backwater conditions. The integrated model works through the following steps iteratively to derive geomorphic change: (1) iRIC model is used to generate a 2D normal water surface; (2) a 1D water surface is developed for the backwater;(3) the normal and backwater surfaces are integrated; (4) an analytical 2D model is established to estimate shear stresses and morphological alterations in the normal, transitional, and backwater zones. The integrated model generates a new digital elevation model based on the estimated erosion and deposition. The resultant topography then serves as the starting point for the next iteration of flow, ultimately modeling geomorphic changes through time. This model was tested on Darby Creek in Metro-Philadelphia, one of the most flood-prone urban areas in the US and the largest freshwater marsh in Pennsylvania.the river increases gradually upstream to form a smooth transition between a quasi-normal flow and standing water, forcing an alteration of the hydraulic conditions. The segment of the river affected by this response-which might exceed hundreds of kilometers in low slope rivers-is called the backwater zone [12].The shift in BW hydraulics causes the water surface slope to decrease independent of the bed slope, resulting in a gradual longitudinal alteration of the flow depth and velocity [13]. The vertical velocity distribution in BW conditions is less divergent compared to the one in a uniform flow [14]. These changes in the hydraulics of the vertical and horizontal flow velocity in BW zones consequently alter the sediment transport processes.Current approaches to BW in morphological studies are primarily based on either measured or estimated hydraulic parameters coupled with the estimated sediment transport [9,11,[15][16][17][18][19][20][21]. The studies of sediment transport in BW conditions are scarce due to the complex dynamics of the sediment transport in BW zones and the lack of field-measured data. The presence of the BW complicates different types of hydrometric measurements including the water surface slope, velocity, and discharge [19]. A lack in measured data is even more severe for transitional areas where the flow state changes from normal to a gradually varied flow [18]. Nittrouer et al. used measured cross sections in the lower Mississippi River to back calculate velocity, shear stress and sediment discharge [22]. They concluded that where the flow transitions to the BW zone, the cross-sectional area increases in the downstream in low-moderate flows, resultin...

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