GeoFlood: Computational Model for Overland Flooding

Core Concepts

GeoFlood is a powerful computational model for accurately predicting flood wave propagation on complex terrain, validated through benchmark tests and historical dam break simulations.

Abstract

The content discusses the development and validation of GeoFlood, a computational model for overland flooding. It covers the background, methodology, benchmark tests, numerical methods, adaptive mesh refinement, and simulation results for various scenarios including dam breaks. The model's efficiency and accuracy in predicting flood extents are highlighted through comparisons with other models like HEC-RAS and GeoClaw. 1. Introduction to GeoFlood: Presents a new open-source software package for solving shallow water equations on mapped grids. Validates the GeoFlood model against standard benchmark tests and historical dam break events. 2. Software for Modeling Overland Flooding: Discusses fundamental building libraries like Clawpack, p4est, and ForestClaw used in GeoFlood. 3. Numerical Methods for Overland Flooding: Explains the governing equations of shallow water wave equations and finite volume discretization techniques. 4. Adaptive Mesh Refinement Using Quadtree Meshing: Describes the multi-resolution grid hierarchy in ForestClaw using non-overlapping fixed-sized grids stored as leaves in a quadtree forest. 5. Benchmark Test Cases: Evaluates GeoFlood's performance in filling floodplain depressions, speed of flood propagation, and dam break simulations. Compares simulation results with HEC-RAS and GeoClaw to validate accuracy. 6. Malpasset Dam Break Simulations: Details the historical background of the Malpasset Dam failure event. Discusses topography data, initial conditions, simulation results, validation against field observations, Google Earth visualization, parallel efficiency analysis.

Stats

The potential Mosul dam failure could cause catastrophic floods affecting millions of people (Filkins, 2016). The Malpasset dam failure resulted in 433 fatalities and significant infrastructure damages (Boudou et al., 2017).

Quotes

Key Insights Distilled From

GeoFlood

by Brian Kyanjo... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15435.pdf

Deeper Inquiries

How can GeoFlood's adaptive mesh refinement improve flood prediction accuracy?

GeoFlood's adaptive mesh refinement plays a crucial role in enhancing flood prediction accuracy by dynamically adjusting the resolution of the computational grid based on specific criteria. This allows for more detailed representation of complex topographical features, such as depressions, embankments, and obstacles, which are critical in determining the flow dynamics during flooding events. By refining the mesh in areas with significant changes in water depth or velocity, GeoFlood can capture fine-scale variations in inundation extent and timing accurately. Furthermore, adaptive mesh refinement enables GeoFlood to focus computational resources where they are most needed. By concentrating grid resolution around key areas of interest, such as dam locations or urban centers prone to flooding, the model can provide more precise predictions in these critical zones. This targeted approach ensures that computational resources are utilized efficiently while maintaining high levels of accuracy in flood simulations. Overall, GeoFlood's adaptive mesh refinement enhances flood prediction accuracy by allowing for finer spatial resolution where it matters most, capturing intricate details of flow behavior over varied terrain and improving overall simulation precision.

What are the implications of using different Manning coefficients in flood modeling software?

The Manning coefficient is a key parameter used to represent channel roughness and resistance to flow within hydraulic models like those used in flood modeling software. The choice of Manning coefficient has significant implications for how water flows through a given area and influences various aspects of flood simulations: Flow Velocity: A higher Manning coefficient indicates greater roughness and resistance to flow, resulting in lower velocities within channels or over surfaces. Conversely, a lower Manning coefficient implies smoother surfaces with less resistance and potentially higher flow velocities. Water Depth: The Manning coefficient affects how water depth changes across a landscape during flooding events. Higher values lead to slower propagation speeds and deeper water depths due to increased frictional losses along the channel bed. Inundation Extent: Different Manning coefficients can influence how far floods spread across an area by altering flow rates and velocities. Higher coefficients may restrict inundation extents compared to lower coefficients that allow faster flows over larger distances. Model Sensitivity: Changes in the Manning coefficient can impact model sensitivity to input parameters like topography or initial conditions. Adjusting this parameter alters how quickly water moves through a system and interacts with its surroundings. 5 .Accuracy vs Computational Cost: Selecting an appropriate Manning coefficient balances model accuracy with computational cost - higher resolutions require more computation but may yield more accurate results depending on local conditions.

How does GeoFlood's parallel efficiency impact its scalability compared to other models?

GeoFlood's parallel efficiency significantly impacts its scalability compared to other models by optimizing resource utilization across multiple processing units effectively. Improved Performance: Efficient parallelization allows GeoFlood to distribute computations seamlessly among processors without idle time or bottlenecks. Enhanced Scalability: With better load balancing mechanisms enabled by parallel efficiency measures, -GeoFlod scales well when increasing processor counts -It maintains performance consistency even at high core counts -Resource Utilization: Parallel efficiency ensures that all available cores contribute meaningfully towards completing tasks efficiently. -Reduced Execution Time: Tasks run concurrently on multiple cores leadingto faster execution times Overall,GoeFlod’sparallel efficieny enhacesits scalabilty makingit adeptat handling large-scalefloo modelling problemswith optimalperformanceandresourceutiliztion