An Alternative Approach for Improving Prediction of Integrated Hydrologic‐Hydraulic Models by Assessing the Impact of Intrinsic Spatial Scales

Water Resources Research

et al. 2023

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Multiphysics urban flood models are commonly used for urban infrastructure planning, risk management, and forecasting (Hemmati et al., 2020;Qi et al., 2021;Rosenzweig et al., 2021). Because these models have many uncertain parameters and rely on assumptions (e.g., the effect of the groundwater flow on urban flooding), it is important to quantify uncertainty in the model-based predictions and forecasts.In recent years, several continent-scale and regional flood modeling approaches were introduced that are based on one-and two-dimensional surface hydraulics equations (Merwade et al., 2008). Flood modeling in urban areas is more challenging because urban systems are generally complex, and their models require a detailed representation of multiple components. Recent advances in computational capabilities have enabled hyper-resolution urban flood predictions using integrated flood models (Saksena et al., 2019). Some examples of these models are the Interconnected Channel and Pond Routing (ICPR), ParFlow, and GSSGA models (Downer et al., 2006;Kollet & Maxwell, 2006; Streamline-Technologies, 2018). The integrated models have the capability to simultaneously calculate urban watershed processes, including channel flow, overland flow, unsaturated flow, groundwater flow, and stormwater flow. Each of these individual processes is simulated using an appropriate set of equations (and the associated parameters) and their numerical approximations. The unknown parameters are a source of parametric uncertainty. The approximations and omitted physical processes result in model uncertainty (Saksena et al., 2020(Saksena et al., , 2021.The effect of the model and parametric uncertainties must be understood, characterized, and quantified to make actionable risk-based decisions in response to intense rainfall events in urban areas. In this work, we investigate the impact of both parametric and model uncertainties on the urban flood predictions of the ICPR model.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Urban Flood Modeling: Uncertainty Quantification and Physics‐Informed Gaussian Processes Regression Forecasting

Kohanpur

Water Resources Research

et al. 2023

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“…These studies have shown that river bathymetry impacts the surface-subsurface hydrodynamics at the reach scale. Hydrologic and hydrodynamic processes aggregate and interact differently as we move from single reach to large river networks spanning an entire watershed (Saksena et al, 2021). Therefore, there is a need to evaluate the influence of river bathymetry on hydrologic processes across large river networks.…”

mentioning

confidence: 99%

“…in Saksena et al (2021) for effectively capturing SW-GW interactions in tightly coupled models by considering the intrinsic scales of the surface and subsurface processes in the model structure. It should be noted that the surface and subsurface parameters are uncalibrated and are identical across different bathymetric configurations to avoid biasing the parameters toward any particular configuration.…”

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confidence: 99%

Incorporating Network Scale River Bathymetry to Improve Characterization of Fluvial Processes in Flood Modeling

Winter

et al. 2022

Preprint

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River bathymetry is critical for simulating fluvial hydrodynamics accurately in flood inundation mapping. Several studies have investigated the impact of poor bathymetric representation on one-dimensional and two-dimensional flow models and concluded that river bathymetry affects hydraulic attributes significantly. Specifically, inaccurate estimation of channel storage capacity may lead to errors in predicting the depth and extent of inundation. Similarly, errors in estimating longitudinal slope affect the magnitude of streamflow and erroneous thalweg representation can contribute to poor estimation of shear and velocity (

Incorporating Network Scale River Bathymetry to Improve Characterization of Fluvial Processes in Flood Modeling

Winter

et al. 2022

Preprint

Self Cite