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Framework for dynamic modelling of urban floods at different topographical resolutions
Urban flood risks and their impacts are expected to increase as urban development in flood prone areas continues and rain intensity increases as a result of climate change while aging drainage infrastructures limit the drainage capacity in existing urban areas. The research presented in this thesis addresses the problem of capturing small-scale features in coarse resolution urban flood models with the aim of improving flood forecasts in geometrically complex urban environments.
Book
A globally applicable framework for compound flood hazard modeling
Coastal river deltas are susceptible to flooding from pluvial,
fluvial, and coastal flood drivers. Compound floods, which result from the
co-occurrence of two or more of these drivers, typically exacerbate impacts
compared to floods from a single driver. While several global flood models
have been developed, these do not account for compound flooding. Local-scale
compound flood models provide state-of-the-art analyses but are hard to
scale to other regions as these typically are based on local datasets.
Hence, there is a need for globally applicable compound flood hazard
modeling. We develop, validate, and apply a framework for compound flood
hazard modeling that accounts for interactions between all drivers. It
consists of the high-resolution 2D hydrodynamic Super-Fast INundation of CoastS (SFINCS) model, which is
automatically set up from global datasets and coupled with a global
hydrodynamic river routing model and a global surge and tide model. To test
the framework, we simulate two historical compound flood events, Tropical
Cyclone Idai and Tropical Cyclone Eloise in the Sofala province of Mozambique, and compare
the simulated flood extents to satellite-derived extents on multiple days
for both events. Compared to the global CaMa-Flood model, the
globally applicable model generally performs better in terms of the critical
success index (−0.01–0.09) and hit rate (0.11–0.22) but worse in
terms of the false-alarm ratio (0.04–0.14). Furthermore, the simulated flood
depth maps are more realistic due to better floodplain connectivity and
provide a more comprehensive picture as direct coastal flooding and pluvial flooding
are simulated. Using the new framework, we determine the dominant flood
drivers and transition zones between flood drivers. These vary significantly
between both events because of differences in the magnitude of and time lag
between the flood drivers. We argue that a wide range of plausible events
should be investigated to obtain a robust understanding of compound flood
interactions, which is important to understand for flood adaptation,
preparedness, and response. As the model setup and coupling is automated,
reproducible, and globally applicable, the presented framework is a
promising step forward towards large-scale compound flood hazard modeling.
Journal Article
Applying the flood vulnerability index as a knowledge base for flood risk assessment
Floods are one of the most common and widely distributed natural risks to life and property worldwide.?There is a need to identify the risk of flooding in flood prone areas to support decisions for flood management from high level planning proposals to detailed design. An important part of modern flood risk management is to assess vulnerability to floods. This assessment can be done only by using a parametric approach.?Worldwide there is a need to enhance our understanding of vulnerability and to also develop methodologies and tools to assess vulnerability.?One of the most important goals of assessing flood vulnerability is to create a readily understandable link between the theoretical concepts of flood vulnerability and the day-to-day decision-making process and to encapsulate this link in an easily accessible tool.?The present book portrays a holistic parametric approach to be used in flood vulnerability assessment and this way to facilitate the consideration of system impacts in water resources decision-making.?The approach was verified in practical applications on different spatial scales and comparison with deterministic approaches. The use of flood vulnerability approach can produce helpful understanding into vulnerability and capacities for using it in planning and implementing projects.
Book
Satellite Video Remote Sensing for Flood Model Validation
Satellite‐based optical video sensors are poised as the next frontier in remote sensing. Satellite video offers the unique advantage of capturing the transient dynamics of floods with the potential to supply hitherto unavailable data for the assessment of hydraulic models. A prerequisite for the successful application of hydraulic models is their proper calibration and validation. In this investigation, we validate 2D flood model predictions using satellite video‐derived flood extents and velocities. Hydraulic simulations of a flood event with a 5‐year return period (discharge of 722 m3 s−1) were conducted using Hydrologic Engineering Center—River Analysis System 2D in the Darling River at Tilpa, Australia. To extract flood extents from satellite video of the studied flood event, we use a hybrid transformer‐encoder, convolutional neural network (CNN)‐decoder deep neural network. We evaluate the influence of test‐time augmentation (TTA)—the application of transformations on test satellite video image ensembles, during deep neural network inference. We employ Large Scale Particle Image Velocimetry (LSPIV) for non‐contact‐based river surface velocity estimation from sequential satellite video frames. When validating hydraulic model simulations using deep neural network segmented flood extents, critical success index peaked at 94% with an average relative improvement of 9.5% when TTA was implemented. We show that TTA offers significant value in deep neural network‐based image segmentation, compensating for aleatoric uncertainties. The correlations between model predictions and LSPIV velocities were reasonable and averaged 0.78. Overall, our investigation demonstrates the potential of optical space‐based video sensors for validating flood models and studying flood dynamics.
Plain Language Summary
Videos of the Earth surface recorded by satellites can enable us to observe and characterize dynamic moving features, such as floods, that would otherwise be very difficult or dangerous to investigate from the ground. Hydrologists often rely on using physics‐based computer models to simulate flood events, but require observational data to make sure these reflect reality accurately. We use artificial intelligence techniques to automatically detect flood extents from satellite video, and track surface features from frame to frame in order to measure how fast the water surface is flowing. Satellite video was collected during opportunistically clear skies in January 2022, along a 6.5 km length of the River Darling in Australia. The flood extent and flow velocities were used to improve numerical model predictions of the flood event. Our findings demonstrate the considerable promise of satellite video to complement existing flood mapping and modeling approaches, and to provide insight into the earth's hydrosphere, particularly in remote locations and during extreme conditions.
Key Points
Satellite video derived flood extents and velocities successfully validate 2D hydraulic model predictions
Test‐time augmentation during deep learning inference improved flood extent delineation and enhanced 2D model validation metrics
Incorporating characterization of discharge uncertainty into hydraulic model predictions resulted in more accurate model validation
Journal Article
Urban storm flood simulation using improved SWMM based on K‐means clustering of parameter samples
To address the two problems of unclear delineation of sub‐catchment and complicated and cumbersome parameter rate determination in the Storm Water Management Model (SWMM), this study proposes a rapid construction method of SWMM based on the principle of single urban functional area combined with K‐means clustering algorithm, The research area is the southern part of Jinshui District, Zhengzhou City. The Hydrological Response Unit (HRU) contains only a single urban functional area, divided by combining the natural and social attributes of the urban surface. Calibrated uncertain parameters from 76 papers were selected as samples, and the K‐means clustering algorithm was used to cluster and calculate the parameter values, to improve the SWMM model, selecting three typical rainfall runoff processes for validation application. The results show that simulated runoff is consistent with measured runoff trends, with the NSE and R2 value scores of the flow processes of the three floods above 0.86 and the, locations and numbers of flooded nodes are consistent with the actual research. This provides a new idea and technical support for the construction of urban flood models in flood prevention and mitigation. The relevant results can provide scientific decision‐making reference for urban flood forecasting and warning.
Journal Article
Floods in a changing climate. Hydrologic modeling
\"Various modeling methodologies are available to aid planning and operational decision making: this book synthesises these, with an emphasis on methodologies applicable in data scarce regions, such as developing countries. Problems included in each chapter, and supported by links to available online data sets and modeling tools, engage the reader with practical applications of the models. Academic researchers in the fields of hydrology, climate change, and environmental science and hazards, and professionals and policy-makers working in hazard mitigation, remote sensing and hydrological engineering will find this an invaluable resource. This volume is the second in a collection of four books on flood disaster management theory and practice within the context of anthropogenic climate change. The others are: Floods in a Changing Climate: Extreme Precipitation by Ramesh Teegavarapu, Floods in a Changing Climate: Inundation Modelling by Giuliano Di Baldassarre and Floods in a Changing Climate: Risk Management by Slodoban Simonoviâc\"-- Provided by publisher.
Book
Developing a Levee Module for Global Flood Modeling With a Reach‐Level Parameterization Approach
Levees are critical infrastructure for mitigating flood hazards worldwide. Despite their importance, current global flood models inadequately consider levees due to the complexity of flow mechanisms involving levees and lack of levee data. This research develops a levee module for global flood modeling and proposes a reach‐level parameterization approach to estimate levee parameters globally. Specifically, we developed a simplified levee module within the CaMa‐Flood flood model, requiring only two levee parameters: levee unprotected fraction and equivalent levee height. We then identified leveed river reaches globally and estimated the levee parameters for these reaches using open‐access land use and levee standard data. Finally, we evaluated the model's performance by comparing changes in river hydrodynamics and flood hazard maps, with and without levees, against observed data or official flood maps from representative case studies. The results showed that (a) the proposed approach successfully identified protected reaches with a hit rate of 82.1% in data‐rich regions, (b) the levee unprotected fraction can be accurately estimated based on land‐use data, and equivalent levee height can be derived from flood defense data using the global flood model, and (c) the enhanced CaMa‐Flood model, incorporating the levee module, accurately simulated both river hydrodynamics and flood hazard mapping, improving the mean Nash‐Sutcliffe Efficiency of water levels from 0.68 to 0.84 and increasing the accuracy of flood hazard mapping from 0.76 to 0.87 in five US regions. Validation on flood mapping in other eight representative regions worldwide confirms robust performance and strong potential for global applications.
Journal Article
Need for judicious selection of runoff inputs in a global flood model
Numerous flood hazard assessment and risk management studies depend on hydrodynamic flood models, which require detailed inputs. However, these models face challenges when assessing flood hazards and risks at national scales due to the unavailability of input data and high computational demands. Recent advancements in global flood models (GFMs) have emerged as promising solutions. These widely adopted GFMs, capable of producing flood characteristics, require runoff input typically derived from land surface models (LSMs) or global hydrological models (GHMs), which are prone to inherit cascading uncertainties. Moreover, the utilization of a single runoff input into a GFM can produce biased and misinterpreted flood hazards due to underestimation or overestimation of GFM outputs. To highlight these implications, the present study examines GFM simulations forced with eight state-of-the-art model runoff datasets, including LSMs, GHMs, and reanalysis observations, uncovering unsafe inter-model flood depth variation (IMDV). Focusing on the flood-prone Mahanadi River Basin (MRB) of India, the study observes that IMDV surpasses the self-help range of humans (0.2 m) for 65% of the MRB region, and exceeds human and vehicle safety thresholds (2 m) for 15% of the region, based on four past flood events from the Dartmouth Flood Observatory. These regions exhibiting high IMDV overlap with densely populated areas, potentially affecting 1.66–3.65 million people. Thus, the injudicious use of runoff in GFM for flood disaster planning can lead to inaccurate flood hazard and risk assessments, significantly affecting populous regions. An alternative approach is recommended, advocating for the use of multiple simulations incorporating diverse runoff datasets. This approach would generate conservative and optimistic flood scenarios, leveraging each model’s strengths. Such comprehensive hazard scenarios would enhance flood management and decision-making for policymakers by addressing the uncertainty and providing possible impacts through risk assessments.
Journal Article
A comparison of global flood models using Sentinel-1 and a change detection approach
Advances in numerical algorithms, improvement of computational power and progress in remote sensing have led to the development of global flood models (GFMs), which promise to be a useful tool for large-scale flood risk management. However, performance and reliability of GFMs, especially in data-scarce regions, is still uncertain, as they are difficult to validate. Here we aim at contributing to develop alternative, more flexible, and consistent methods for GFM validation by applying a change detection analysis on synthetic aperture radar (CD-SAR) imagery obtained from the Sentinel-1 imagery, on a cloud-based geospatial analysis platform. The study addresses two main objectives. First, to validate four widely adopted GFMs with flood maps generated through the proposed CD-SAR approach. This exercise was conducted for eight different large river basins on four continents, to account for a diverse range of hydro-climatic environments. Second, to compare CD-SAR-derived flood maps with those obtained from alternative remote sensing sources. These comparative results offer valuable insights into the reliability of CD-SAR data as a validation tool, more specifically how it stacks up against flood maps generated by other remote sensing techniques.
Journal Article