Assessing the Performance of LISFLOOD-FP and SWMM for a Small Watershed with Scarce Data Availability

Sadeghi, Farzaneh; Rubinato, Matteo; Goerke, Marcel; Hart, James

doi:10.3390/w14050748

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…When conducting simulations of relatively shallow urban surface water inundation, the kinematic wave model has certain limitations, requiring several significant assumptions to be made, and it has a low level of accuracy itself. Therefore, the diffusion wave model [91][92][93] and the simplified inertial equation model [94,95] are considered to be more practical and commonly used simplified models in the field of flood disaster simulation and risk perception. However, simplified models may lead to inaccurate computational results.…”

Section: Isolated Scenario Analysis (Isa)mentioning

confidence: 99%

Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Sun,

Zhang,

Ruan

et al. 2024

Remote Sensing

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Coastal regions, increasingly threatened by floods due to climate-change-driven extreme weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In response to this research gap, we conducted a comprehensive bibliometric analysis and thorough visualization and mapping of studies of compound flooding risk in coastal cities over the period 2014–2022, using VOSviewer and CiteSpace to analyze 407 publications in the Web of Science Core Collection database. The analytical results reveal two persistent research topics: the way to explore the return periods or joint probabilities of flood drivers using statistical modeling, and the quantification of flood risk with different return periods through numerical simulation. This article examines critical causes of compound coastal flooding, outlines the principal methodologies, details each method’s features, and compares their strengths, limitations, and uncertainties. This paper advocates for an integrated approach encompassing climate change, ocean–land systems, topography, human activity, land use, and hazard chains to enhance our understanding of flood risk mechanisms. This includes adopting an Earth system modeling framework with holistic coupling of Earth system components, merging process-based and data-driven models, enhancing model grid resolution, refining dynamical frameworks, comparing complex physical models with more straightforward methods, and exploring advanced data assimilation, machine learning, and quasi-real-time forecasting for researchers and emergency responders.

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Section: Isolated Scenario Analysis (Isa)mentioning

confidence: 99%

Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Sun,

Zhang,

Ruan

et al. 2024

Remote Sensing

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“…It has been used in numerous studies in Europe and is the main hydrological model in the European Flood Awareness System (EFAS) [37]. LISFLOOD has also been coupled with other storm water management models (SWMMs) [69]) and The Soil and Water Assessment Tool (SWAT) [70] for flood prediction.…”

Section: Deterministic Modelsmentioning

confidence: 99%

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Byaruhanga,

Kibirige,

Gokool

et al. 2024

Water

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Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems.

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“…The robustness of similar model approaches (e.g., LISFLOOD-FP, FLO-2D, Floodmap) has been validated and applied in 2-D floodplains in coastal areas or fluvial landforms [29,80,83,89]. CoastFLOOD also follows a simplistic finite difference scheme for hydraulic flow inundation, running on very fine resolution raster grids, able to reproduce the surge-induced 2-D flood on the coast [17,18].…”

Section: Numerical Model For Hydraulic Flow In Coastal Floodingmentioning

confidence: 99%

CoastFLOOD: A High-Resolution Model for the Simulation of Coastal Inundation Due to Storm Surges

et al. 2023

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Storm surges due to severe weather events threaten low-land littoral areas by increasing the risk of seawater inundation of coastal floodplains. In this paper, we present recent developments of a numerical modelling system for coastal inundation induced by sea level elevation due to storm surges enhanced by storm tides. The proposed numerical code (CoastFLOOD) performs high-resolution (5 m × 5 m) raster-based, storage-cell modelling of coastal inundation by Manning-type equations in decoupled 2-D formulation at local-scale (20 km × 20 km) lowland littoral floodplains. It is fed either by outputs of either regional-scale storm surge simulations or satellite altimetry data for the sea level anomaly. The presented case studies refer to model applications at 10 selected coastal sites of the Ionian Sea (east-central Mediterranean Sea). The implemented regular Cartesian grids (up to 5 m) are based on Digital Elevation/Surface Models (DEM/DSM) of the Hellenic Cadastre. New updated features of the model are discussed herein concerning the detailed surveying of terrain roughness and bottom friction, the expansion of Dirichlet boundary conditions for coastal currents (besides sea level), and the enhancement of wet/dry cell techniques for flood front propagation over steep water slopes. Verification of the model is performed by comparisons against satellite ocean color observations (Sentinel-2 images) and estimated flooded areas by the Normalized Difference Water Index (NDWI). The qualitative comparisons are acceptable, i.e., the modelled flooded areas contain all wet area estimations by NDWI. CoastFLOOD results are also compared to a simplified, static level, “bathtub” inundation approach with hydraulic connectivity revealing very good agreement (goodness-of-fit > 0.95). Furthermore, we show that proper treatment of bottom roughness referring to realistic Land Cover datasets provide more realistic estimations of the maximum flood extent timeframe.

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Assessing the Performance of LISFLOOD-FP and SWMM for a Small Watershed with Scarce Data Availability

Cited by 9 publications

References 24 publications

Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

CoastFLOOD: A High-Resolution Model for the Simulation of Coastal Inundation Due to Storm Surges

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