The subject of the study is the considerable uncertainty in determining flood risk when long-term climate change projections are developed. Risk management decision-making involves comparing options based on their benefits and costs. The purpose of the analysis is to reveal the uncertainty robustness of alternative flood protection measures. The treatment of different sources of uncertainty is done by using probabilistic net present value (NPV) analysis as well as by using Information-gap decision theory (IGDT). The case study is a settlement in northern Bulgaria with a record of severe flooding in the past, for which different climate change projections are generated under RCP 4.5 and RCP 8.5 scenarios. The behaviour of three civil protection options under these uncertainty conditions is investigated for an extended 30-year time period to 2050. A probabilistic analysis with NPV performance criterion is performed sequentially, followed by Info-gap decision theory analysis.

After discussing the results, the advantages and disadvantages of the two methods are compared. Some limitations and advantages of the Information gap theory are discussed. Finally, it is highlighted that when making decisions about long-term flood protection, it is recommended to use multiple methods that differ in data and assumptions, necessarily taking into account the hydrological uncertainty arising from climate change, which can radically change our choices.

How to cite: Mavrova-Guirguinova, M.: Impact of Uncertainty on the Choice of Long-Term Flood Protection Option under Climate Change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16798, https://doi.org/10.5194/egusphere-egu24-16798, 2024.

Increasingly frequent and intense flood events, combined with the remarkable industrialization process of cities, are placing transportation networks under stress. The loads that roads and railways must resist are nowadays often greater than those considered for their design; furthermore, their state of ageing is such that any disturbance (flood, earthquake, landslide) could cause a total or partial interruption of traffic resulting in socio-economic losses.
Bridges represent the most vulnerable component of a transport system and their failure can compromise the functionality of the entire network, as well as causing loss of life. During floods, bridges can be partially or completely submerged, having to withstand higher hydrodynamic loads which can lead to the collapse of the structure itself. Furthermore, accumulations of large wood and scour total around the bridge piers can reduce the load-bearing capacity of the structure and therefore its structural integrity.
In this study, we investigated the hydrodynamic actions and the 3-dimensional flow field at a model bridge (comprising deck and pier) using CFD (Computational Fluid Dynamics) modelling. Drag and lift forces acting on the rectangular-shaped deck were estimated for different submergence values to evaluate the structure's maximum permissible load. In particular, drag and lift coefficients were calculated by simulating various flow conditions (Froude number varying between  0.16 and 0.50) and adopting three different turbulence models (RNG, k-ε, k-ω).
In addition, the effect on the drag coefficient of the accumulation of large wood around the pier was also examined, considering different geometries. Numerical simulations, performed for both fixed and live river bed conditions, were validated using experimental data. However, the trends of the synthetic curves constructed so far have presented characteristics similar to those present in the literature, with all positive values ​​for the drag coefficient and negative ​​for the lift coefficient. 
The emerging evaluations allow us to provide useful indications to designers to evaluate the possible state of stresses on existing bridges and improve knowledge for designing new ones.

How to cite: Petruccelli, N., Panici, D., Domeneghetti, A., and Brath, A.: CFD modelling to investigate hydrodynamic forces on bridges in case of submergence and material deposition , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1081, https://doi.org/10.5194/egusphere-egu24-1081, 2024.

Levees are linear structures that can be thousands of kilometers long and play a very important role in flood protection. They are usually monitored by traditional direct survey techniques, such as CPTU or coring, or piezometers, which provide high accuracy, but are localized and performed in predetermined locations.

As a result, long distances between investigated sections limit the detailed analysis of the entire structure. In addition, predetermined locations may not cover areas of actual potential weakness. Recently, new survey technologies from aerial media (drones) have been successfully applied to obtain a first level of levee investigation in order to identify the location of possible weak areas or potential locations of levee failure, so as to plan further local investigations in those areas.

Usually, levee failures are localized in the presence of:

(i) concrete or other materials structures passing the levee;

(ii) large trees, which can be dangerous because their roots are a preferred route for water infiltration. In addition, at higher erosion levels of the river bank, large trees can promote bank collapse due to their weight (i.e. cantilever failure);

(iii) sections where unfavorable conditions of the levee body, such as soils with high permeability or the presence of animal burrows crossing the levee or obstructed drains, prevent proper drainage and bring the phreatic surface close to the levee surface.

From previous experience, we have noticed that several times levee failures have occurred at sections previously vegetated by reeds. Reed canes usually grow on sandy soils and, in addition, are characterized by very deep and large roots, possible routes of localized infiltration through the body of the levee. From these observations comes the idea of using reedbeds as indicators of sandy soils and possible weak levee sections;

Thus, we performed two UAV-supported surveys on the same test area aimed at identifying the position and extension of the reeds vegetated areas, in combination with local on-site surveys with soil sampling along levee transversal sections, to compare and combine the obtained results. The RGB orthophotos obtained by the two surveys have been elaborated to determine the DSM and the vegetation cover map of the embankment, to compare them in different seasons. The obtained data have been calibrated with on-site surveys conducted by vegetation experts. To facilitate the identification of reedbeds, the first campaign has been carried out in winter, when reedbeds are yellowish in color, unlike short grass. In areas identified as reedbed vegetated, the soil has been sampled by coring and fully classified in the geotechnical laboratory to check if reedbed can effectively be an indicator of sandy soils. Similarly, other samples have been taken from sections not covered by reeds for comparison.

The final aim is to test the possibility of using vegetation maps as an indicator of weak sections of the embankment, thus to develop an innovative method of low-cost aerial monitoring of levee structures that can provide an initial state of information and identify areas in need of further direct investigation in order to define the necessary maintenance works, decreasing associated risks.

How to cite: Dalla Santa, G. and Simonini, P.: Reeds influence of levee hazards: detection through UAV survey and soil geotechnical analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8071, https://doi.org/10.5194/egusphere-egu24-8071, 2024.

Assessing flood risk requires the combination of flood hazard, exposure, and vulnerability.  Hence, flood hazard is a key component of flood risk assessments. As flood propagation is impacted by hydraulic structures built along the river, flood defense such as levees have gained attention as they are rarely included in large scaled flood risk assessments. However, flood events such as hurricane Katrina showcased the impact that levee failure has on flow depth, velocity, and flood extent. Therefore, its consideration should be regularly implemented in flood risk assessments. However, with current flood risk assessment methods, considering different levee failure scenarios results in numerous flood scenarios, simulations, and hazard maps. The multiplication of simulations and maps increases the complexity of flood risk management. We propose to improve flood hazard assessments by considering a single probabilistic flood map accounting for several flood events and levee breaching scenarios. For flood events enabling the performance assessment of the levee (i.e. levee breaching), we assessed levee failure probabilities, associating each levee segment to a fragility curve. Then, we defined breaching and non-breaching scenarios and ran flood simulations using HEC-RAS and its integrated parametric levee breaching model. We propose a new method to compute flood scenario probabilities and flood exceedance probabilities. The cumulative flood exceedance probability provides a curve for every location of the flooded area. Using GIS, we applied this method to the entire flooded area, resulting in an interactive flood hazard map. An application to the Etobicoke Creek located in the Greater Toronto Area showed that this new approach provides an operational levee breaching flood hazard method that can be used in integrated flood risk assessments.

How to cite: Mainguenaud, F., Khan, U., Peyras, L., Carvajal, C., Sharma, J., and Beullac, B.: Estimating flood exceedance probabilities for several levee breach scenarios for an urban riverine environment in Toronto, Canada, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12935, https://doi.org/10.5194/egusphere-egu24-12935, 2024.

Floods are among the most frequent and damaging natural hazards, affecting millions of people worldwide, and the risk of catastrophic losses due to flooding is expected to increase as a result of climate change. The possibility of predicting and estimating the expected fatalities in flood-prone regions is among the top priorities of decision-makers in flood risk management. Thus, predicting the conditions leading to loss of life is crucial for assessing the risk to the population. Here we focus on the Po River District in Northern Italy which covers the largest Italian hydrographic basin. We demonstrate that the occurrence of flood-related fatalities can be estimated by utilizing a random forest (RF) algorithm applied to a dataset of fatalities that occurred in this area from 1970 to 2019. This method relies on nine explanatory variables that describe the hazard intensity, and the environmental and sociodemographic conditions leading to fatalities. The proposed model is a primary attempt to estimate the probability of flood-related fatalities in the Italian context, and it provides a proxy for the quantitative estimation of flood risk to the population.

How to cite: Yazdani, M., N. Gencarelli, C., Salvati, P., and Molinari, D.: Modeling flood fatalities in the Italian context: an empirical approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12195, https://doi.org/10.5194/egusphere-egu24-12195, 2024.

Developing reliable and efficient flood modelling systems on a large scale is crucial for addressing errors and inconsistencies in both observations and modelling. However, the computational demands of hydrodynamic models have constrained their widespread application to coarse resolutions (30m-1km), compromising accuracy by neglecting the local and small-scale features that may significantly influence flooding, especially in urban areas. Furthermore, traditional models struggle to effectively incorporate river bathymetry, especially given the significant flood volume conveyed by the river channel during floods. These models often rely on surveyed cross-sections for river channel representation, leading to missing topography between cross-sections and hindering the resolution of complex floodplain flow paths. To resolve small-scale effects in limited areas while simulating large domains, grid adaptation methodologies are implemented in this project to locally adjust the resolution of the computation in a static or a dynamic way. A hybrid 1D-2D flood model is developed, incorporating the static/dynamic adaptive mesh generation and an integrated sub-/super grid channel model. The sub-/super grid channel is applied to accommodate situations where river channel width exceeds or fall below the grid resolution. Parallelized with GPU architecture, the performance of hybrid 1D-2D with either static or dynamics nonuniform structured grid was thoroughly evaluated, benchmarked with the full resolution CPU solver, shedding light on their effectiveness in enhancing flood modelling approach.

How to cite: Rong, Y., Bates, P., and Neal, J.: Towards a large-scale locally relevant flood modelling using adaptive mesh generation and an integrated sub-/super grid channel solver, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6787, https://doi.org/10.5194/egusphere-egu24-6787, 2024.

Climate change projections for 2030 indicate a concerning increase in the frequency of floods, which is expected to result in significant economic damages and losses on a global scale. The growth of urbanization has indeed increased flood risk, highlighting the need for a prompt evaluation of economic losses to facilitate rapid response and effective reconstruction. However, providing timely and accurate economic damage assessment immediately after a flood event is difficult and associated with high uncertainty. Remote sensing  data can support this task, but challenges such as cloud cover, infrequent return times from satellites, and the lack of ground truth data make supervised approaches challenging. To address these challenges, we propose a new economic damage assessment approach based on the analysis of multi-temporal and multi-source, Synthetic Aperture Radar (SAR) images before and after the flood peak with an unsupervised change detection method. This method utilizes computer vision techniques, specifically a pixel-based approach with SAR data (Sentinel-1 and TerraSAR-X/TanDEM-X) to monitor changes in buildings and the flood extension. It employs various threshold techniques and parameters to determine the optimal threshold values for highlighting changes and the presence of water. By using this method, our aim is to obtain an economic model based on pixels, which represents the volume of water surrounding or on each building and the flood extension. The purpose of this study is to support governments in decision-making processes and enable insurers to efficiently assess and compensate for damages caused by flood events. 

How to cite: Eudaric, J., Camero, A., Rafiezadeh Shahi, K., Kreibich, H., Martinis, S., and Zhu, X. X.: Rapid unsupervised economic assessment of urban flood damage using SAR images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-532, https://doi.org/10.5194/egusphere-egu24-532, 2024.

Urban flood modeling within complex urban environments demands sophisticated methodologies. While 2D computational models have historically served as foundational tools, their inherent limitations in capturing the intricate three-dimensional dynamics necessitate further exploration. Our research endeavors to expand this understanding by delving into 3D computational simulations, providing a more holistic perspective on urban flood dynamics.

In the current research, we conducted 3D simulations to replicate urban flood processes, drawing comparisons with earlier 2D modeling results and experimental observations. The simulations were executed considering various urban layouts and turbulence closure models. The urban layouts include two groups, totaling 13 architectural models. These models feature varying numbers or positions of openings on their exterior walls to represent architectural elements such as doors and windows that could allow floodwaters to enter in the interior of the buildings. As for the turbulence equations, k-omega SST and k-epsilon were considered. By analyzing the surface velocity, flow depth, and flowrate distribution, preliminary findings indicate that 3D simulations offer enhanced accuracy in capturing intricate flow patterns within urban settings compared to their 2D counterparts. Moreover, the tested simulations from various turbulence models influence the 2D and 3D simulations in different ways. This direct comparison allowed us to dissect and understand the influence of turbulence modeling on the accuracy of 3D simulations, thereby enhancing the robustness of our findings.

After obtaining the relevant results, we applied them to flood risk analysis. Compared to traditional 2D analyses, we derived some new insights to guide informed decision-making, enhancing the applicability of our approach. By integrating sophisticated modeling techniques and risk evaluations, this study paves the way for more resilient and adaptive urban planning strategies, ensuring safer and more sustainable urban environments in the face of increasing flood challenges.

How to cite: Yu, S., Li, X., Kitsikoudis, V., Dellinger, G., and Dewals, B.: Is 3D modelling necessary for simulating long-duration urban flooding？, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9050, https://doi.org/10.5194/egusphere-egu24-9050, 2024.

Urban pluvial floods are one of the most common water-related hazards and are going to become more frequent and severe looking at the upsetting climate projections. These events mainly occur due to intense and short precipitation events, leading to the overload of the sewer network and resulting in physical, economic, and even human losses. To address this hazard, effective methods are needed to estimate the scale and impact of pluvial flood events and to develop mitigation strategies. In this context, 2D/1D dual drainage models have become one of the most useful tools for these purposes, being able to simulate all hydraulic phenomena occurring on and beneath the surface. However, these models require detailed information about the topography and geometrical specifications of the sewer network, which are not always readily accessible or, when available, are often incomplete or of poor quality, particularly in large urban environments.

In this work, considering that pluvial flood studies are becoming more popular and several numerical tools are available, we wanted to address a recurrent question raised by the flood modeler community: is the effort/level of complexity of implementing a detailed dual drainage model worth it? To answer this question, we assess the influence of sewer network data quality on the results of water depth and velocity obtained with a 2D/1D dual drainage model applied to urban flood modelling. For this purpose, an ad-hoc 2D/1D hydraulic model was implemented to simulate the complex network system of the city of Differdange (LU) exploiting the recently developed Iber-SWMM. This city was chosen as study case because it has experienced several flooding events in recent years, such as those recorded in 2021, and it has an extensive dataset of detailed geospatial data available, enabling the setup of a high-resolution resolution and fully coupled 2D/1D dual drainage model.

Sewer network links were classified based on their physical properties, such as diameter and length. The sewer network layout was gradually simplified, starting from the minor links to the more complex segments of the network, obtaining new simplified versions of the network that could represent incomplete or poor-quality scenarios. These simplified versions were successively implemented in the 2D/1D model. The comparison between the results of the complete and comprehensive model and the simplified scenarios reveals the impacts of the quality of the sewer network information on pluvial flood modeling.

How to cite: Montalvo, C., Tamagnone, P., and Cea, L.: Assessment of sewer network data quality on urban pluvial flood modeling with a 2D/1D dual drainage model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11521, https://doi.org/10.5194/egusphere-egu24-11521, 2024.

Recent studies on climate change show an increasing trend in the frequency of extreme weather events (IPCC, 2021; Tradowsky et al., 2023). These include storms with high-intensity precipitation, known as heavy rainfall. During those events the amounts of precipitation can be so high in a very short period of time that catastrophic flooding can also develop far away from rivers and lakes. Heavy rainfall events have occurred more frequently in Germany in recent years, resulting in severe damage and therefore focusing attention on risk management and prevention.

Contributing to an optimal preparation for the consequences of heavy rainfall events the Federal Agency for Cartography and Geodesy (BKG) is working with federal and state authorities to develop a Germany-wide indication map representing simulated flood situations after heavy rainfall events based on standardized guidelines. Once the mapping has been completed within the first half of 2024, it will be freely available as OpenData to politicians, the public administration and the general public for damage prevention and civil protection.

Geodata of the federal and state governments are essential for the hydronumerical two-dimensional modelling. A digital terrain model with a grid width of one meter forms the basis. Road culverts with corresponding dimensions, 3D building models, pumping stations as well as land cover data representing the surface roughness are integrated into this model in order to achieve a hydrologically effective modification and thus a realistic discharge.

The heavy rainfall indication map shows realistic simulation events for possible flooding scenarios that follow the heavy rainfall index according to Schmitt et al., 2018. The index describes the hazardous character of heavy rainfall events based on the return period and is commonly used in heavy rainfall risk communication by German federal and state authorities. Two scenarios are simulated: First, a 100-year event based on KOSTRA data from the German Weather Service (DWD), a dataset including regionalized precipitation heights as a function of precipitation duration and annularity. Second, an extreme heavy rainfall event with a precipitation of 100 mm/h. For both scenarios flood depths, flow velocities and flow directions are simulated.

The indication map for heavy rainfall provides an initial assessment of the risk potential, which, in combination with existing local expertise, should considerably simplify the planning of measures. It serves as an important tool for identifying areas at risk from heavy rainfall. This enables local authorities, planners and emergency services throughout Germany to derive appropriate measures, both preventively and in the event of an actual disaster.

References

Intergovernmental Panel on Climate Change (Ed.): Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, <doi:10.1017/9781009157896>.

Schmitt, T. G. et al.: Einheitliches Konzept zur Bewertung von Starkregenereignissen mittels Starkregenindex, KA Korrespondenz Abwasser, Abfall, 2018(65), Nr. 2.

Tradowsky, J.S., Philip, S.Y., Kreienkamp, F. et al.: Attribution of the heavy rainfall events leading to severe flooding in Western Europe during July 2021. Climatic Change 176, 90 (2023). <https://doi.org/10.1007/s10584-023-03502-7>.

How to cite: Wimmer, L., Hovenbitzer, M., and Merita, P.: Establishing a Germany-wide Standardized Indication Map Representing the Flood Situation Caused by Heavy Rainfall, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15605, https://doi.org/10.5194/egusphere-egu24-15605, 2024.

Knowing the nature of the flood hazard is a crucial factor for improving the resilience of the urban areas, since awareness, preparedness and early warning systems are based on the scientific tools such as the 2D depth-averaged shallow water models. Inland flood hazard primarily stems from pluvial and fluvial inundations, typically modeled separately respecting the pertaining spatial domains of the assessment, namely the urban areas and the riverine floodplains. Considering the high computational power and efficiency of both hardware and codes, the catchment scale hydrological-hydrodynamic modeling is becoming an increasingly adopted approach in flood hazard assessments. Since a complete rainfall-induced routing is preserved, these simulators determine fluvial, pluvial and compound inundations caused by heavy storm events within the entire watershed.

However, this approach leads to flood extent maps in which the inundations such as those resulting from pluvial and fluvial processes, are usually not differentiated, even if significant disparity in the space-time scales and volumes of water are involved. Indeed, these two hazards follow distinct normative and regulatory flood risk management rules among different countries. 

With such a rationale we established a tracer-aided criterion to systematically categorize and map pluvial and fluvial hazard in a catchment scale shallow water model, exploiting the advection process of a conservative tracer. The physically based methodology, implemented in the GPU-parallelized Iber+ software and its water-quality module (IberWQ+), is applied in a small urban catchment for multiple probabilistic scenarios. The results demonstrate the effectiveness of nesting transport and shallow water equations, univocally discretizing the two inundation sources in function of the computational cells reached by the tracer. This enables to define the spatial domains of the pluvial and fluvial processes, providing valuable insights for holistic catchment-scale flood risk management. Additionally, the advancements achieved by the proposed method are showcased in comparison to commonly employed modeling techniques for mapping fluvial inundations. As the tracers continue to improve our understanding of catchment sciences, we conceptualized them role through an abstraction that can aid surface hydrodynamic modelling to identify pluvial and fluvial sources of hazard.

How to cite: Perrini, P., Cea, L., Gioia, A., and Iacobellis, V.: A tracer-aided criterion to discretize pluvial and fluvial flood hazard maps in catchment scale shallow water models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9555, https://doi.org/10.5194/egusphere-egu24-9555, 2024.

Flood model predictions are becoming increasingly available online through open access flood risk maps and communications. While these predictions are important for flood management, their inherent uncertainty presents a considerable risk for real estate markets, a leading indicator of macroeconomic performance.  We, therefore, need to understand the factors influencing real estate demand in an era of open access flood model predictions. Here, we investigate the role of gender, education, employment, place of residence, caring responsibilities, income, insurance, location preferences, level of risk aversion, and flood experience and awareness on coastal real estate demand decisions in the UK in response to flood model predictions. Here, our objective is test whether access to flood predictions is a leading driver of real estate demand decisions or whether alternative factors influence how people perceive such predictions. We achieve this by applying an inter-disciplinary approach, involving numerical flood modelling, a novel experimental willingness-to-pay real estate survey of UK residents in response to flood model outputs, statistical and geospatial modelling, and thematic analysis. Our preliminary findings indicate that access to flood model predictions is the primary factor influencing real estate demand decisions, whereas alternative factors considered have negligible impact. Such preliminary findings suggest that we need to re-think how flood model predictions are communicated in order to minimise real estate risks.  

How to cite: Seenath, A., Mahadeo, S. M. R., Blackett, M., and Catterson, J.: Drivers of coastal real estate demand under flood model predictions in the UK, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1594, https://doi.org/10.5194/egusphere-egu24-1594, 2024.

The lower valley of the Tagus River, one of the most important rivers in the Iberian Peninsula, is a particularly relevant and vulnerable area in terms of flood impact. This valley is characterized by a flattened and large alluvial plain, which implies that floods can affect large areas of territory, causing significant damage and affecting a large number of people. Although several floods have occurred in the lower Tagus valley, the one in February 1979 stands out, since the vast flooded area affected around 10,000 people, many of whom were evacuated or made homeless. The Tagus River flow in its lower valley is controlled, to a large extent, by the functioning of the Alcántara dam, which has the largest water storage in the Tagus basin. In this context, this study aims to develop strategies to take advantage of this infrastructure to effectively mitigate floods in the lower Tagus valley. For that, dam operating strategies, focused on flood mitigation, are developed sustained on a sequence of logical principles, such as avoiding inducing man-made floods or maintaining average water storage similar to the actual one. The effectiveness of the proposed strategies, in terms of flood mitigation, is analyzed by applying the Iber+ hydrodynamic model. For this, the numerical model is validated in the lower Tagus valley by evaluating its ability to reproduce the outstanding flood of 1979. Additionally, several Digital Elevation Models (DEMs) are also analyzed to determine which is the most accurate for the area under scope. The results show that Iber+ model, coupled with Copernicus DEM, is able to provide an efficient reproduction of this flood. In particular, the simulation shows good agreement with some descriptions and watermarks available for the 1979 event. This also allows the analysis of this historical event from a hydrologic-hydraulic perspective, which contributes to improving knowledge and understanding of how floods occur and develop in the lower Tagus valley.

Regarding flood mitigation, results indicate that, since 1970, when data is available, the frequency of floods is reduced by more than 80%, compared to the natural flow regime, with the application of the proposed strategies. In addition, the mitigation of the most extreme floods that occurred during the analyzed period, is also achieved. In particular, peak river flows are reduced for the most extreme events. This implies that flood extension is reduced by around 5-10% in the lower Tagus valley. A more efficient mitigation is achieved for flood indicators closely linked to the damage caused by these events. Thus, water depth is reduced by around 25% and water velocity by around 25-30%, in the flooded areas, for the most extreme events. This corroborates the effectiveness of the proposed dam operating strategies to mitigate floods in the lower Tagus valley through an adequate dam functioning.

The developed proposal provides an affordable approach to flood mitigation in comparison with the construction of additional structural measures, which could also be applicable to other areas vulnerable to floods affected by dam-regulated rivers.

How to cite: Fernández-Nóvoa, D., Ramos, A. M., González-Cao, J., García-Feal, O., Catita, C., Gómez-Gesteira, M., and Trigo, R. M.: Dam operating strategies and hydrodynamic modeling to mitigate floods in the lower Tagus valley similar to the 1979 catastrophic event , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6509, https://doi.org/10.5194/egusphere-egu24-6509, 2024.

Land surface topography plays an essential role in flood plain inundation modeling. High-resolution digital surface models (DSMs) based on LiDAR surveys have become increasingly accessible in various geographical areas. Nevertheless, common practice involves filtering out land surface macrostructures, such as trees and buildings, by using obtained digital terrain models (DTMs) to represent the land surface hydraulic geometry. This is done by letting resistance coefficients represent the effects of both micro and macrostructures on surface flow propagation. In addition, significant information loss is observed when digital terrain models are coarsened for computational efficiency.

In the present study, physically meaningful unstructured meshes are automatically extracted from high-resolution digital surface models to explicitly describe land surface macrostructures. This is achieved by extracting relevant ridges at a selected level of representation without applying any coarsening or depression filling pre-processing. The effects of these macrostructures on floodwater propagation are evaluated by comparing simulations obtained by using digital terrain models and related Manning coefficients, simulations obtained by using digital surface models representing land surface macrostructures and related Manning coefficients, and observations for a real flood inundation event occurred after a levee failure in the lowlands adjoining the Panaro River in Northern Italy in 2020.

The explicit description of land surface macrostructures based on a 1-m digital surface model is found to yield a 42% improvement in the prediction of flooded area extent, a 36% improvement in the prediction of flooded areal position, and a 24% improvement in the prediction of flood plain inundation travel time with respect to the case in which resistance coefficients representing both land surface micro and macrostructures are used. Unstructured meshing of land surface macrostructures based on extracted ridge networks is essential for achieving a detailed description of land surface hydraulic geometry without altering the original topographic data, while also preserving computational efficiency. The obtained results highlight the role of natural and human-made macrotopographic structures in delineating flood plain inundation models and generating flood hazard mapping. These tools represent valuable assets in the context of Emergency Action Planning (EAP) and prevention strategies.

How to cite: Pizzileo, S., Moretti, G., and Orlandini, S.: Flood plain inundation modeling with explicit description of land surface macrotopography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3629, https://doi.org/10.5194/egusphere-egu24-3629, 2024.

Recently, detecting flooded areas in CCTV images was performed based on semantic segmentation models (e.g., U-Net, FCN, etc.). However, these flooded area detection techniques are based on large-scale manually annotated images, which consume manpower and time. Image augmentation is one of the ways to overcome the limitations mentioned above. Some previous studies have used image augmentation to improve the performance of flooded area detection by combining two or more methods. However, there has been no study quantifying which augmentation methods are reasonable. This study aims to verify which image augmentation method is reasonable to improve the performance of urban flooded area detection techniques. First, this study develops a flood area detection technology corresponding to training images augmented with five different methods (Brightness, Blur, Contrast, Rotation, Crop). Subsequently, the performance changes for each technique were quantified, and characteristics related to the performance variations of each method were examined.

How to cite: Kim, S. W., Kwon, S. H., Jun, S., and Jung, D.: Sensitivity Analysis of Image Augmentation Methods to Improve Flooded Area Detection Performance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18287, https://doi.org/10.5194/egusphere-egu24-18287, 2024.