Federal Emergency Management Agency (FEMA) introduced Risk Rating 2.0, a new risk-based premium approach, on October 1, 2021, for new policies and on April 1, 2022, for existing policies. Risk rating 2.0 considers the geographic attributes (e.g. distance to the lake, river, coast), building attributes (e.g. foundation type, first-floor height), and policy attributes (e.g. coverage and deductible limit) by coverage (i.e., building and contents) and perils (i.e., pluvial and fluvial flooding, storm surge, tsunami, great lake, and coastal erosion) to estimate risk premiums. In this review study, we conduct exploratory data analysis and visualization of the rating factors released by FEMA to better understand the risk premium. The associated rating factors are multiplied and summed by coverage to get the initial premium without fees for each structure. As the rating factors are multiplicative, lower factors contribute to lower risk premiums. The rating factors decrease with increasing distance from flood sources.

The states in the USA are categorized into five segments (e.g. Gulf coast states are categorized as segment 1). A base rate is applied to each state by single-family home indicator and perils for levee and non-levee protected areas. The factors are then distributed by territory where each HUC12 is assigned a factor by peril. Inland flood from pluvial and fluvial sources is applicable for all the states where single-family homes are not levee protected. The effect of the inland flood is considered for structures in segment 1 where the distance to the river is less than 13,500 meters. Storm Surge flooding is considered within 11,000 meters of the Gulf coast for non-barrier islands. Tsunami flooding is considered for structures located in coastal CA, OR, WA, AK, AS, GU/MP, and HI. Great Lake flooding is considered for structures located within 8,500 meters of the Great Lake. Coastal erosion is considered for structures located within 100 meters of the coastline.   

The elevation of a structure is an important indicator for estimating risk premium. The higher the elevation of the structure relative to flood sources, the lower the risk factors. The occupancy affects the premium where single-family home masonry structure has a lower rating factor than frame structure. A higher floor of interest has lower factors, lowering the premium for all perils except coastal erosion. The foundation type also affects the factors where Slab foundation has lower factors than Crawlspace foundation, hence lower risk premium. Another addition is elevating the machinery and equipment above the first floor which reduces the initial premium without fees by 5%.  

Individual and community level flood mitigation reduces risk rating 2 insurance premium. Elevating first-floor height (FFH) to 1, 2, and 3 feet above ground reduces the initial premium without fees by 10, 19, and 27.1 percent, respectively, compared to FFH of 0 feet. Community Rating System (CRS) discount reduces the initial premium without fees between 5% to 45% based on CRS class. The information presented in this study will help homeowners, community developers, and government agencies to understand the effect of each attribute on risk premiums.

How to cite: Rahim, M. A., Mostafiz, R. B., and Friedland, C.: Disseminating Flood Risk Information in the USA through Risk Rating 2.0, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16893, https://doi.org/10.5194/egusphere-egu23-16893, 2023.

Remote sensing technology and the resulting high resolution geospatial data now allow for a detailed description of urban landscape, advancing the development of raster-based flood models. Previous studies have highlighted the critical role of finely resolved and accurate terrain data (5m or less) in capturing flow patterns in urban areas. However, using a uniform fine grid resolution over a rectangular domain generally results in dense grids and leads to large computational costs. The small cell size is often an overspecification for rural regions where the flow processes are changing much less rapidly. Unstructured grid models resolve this issue and trade off more complex programming and slower operation against being able to represent a given problem with fewer computational elements. An alternative solution has been recently proposed to apply the non-uniform structured grid, with fine grids covering only the regions where this detail is a necessity, for example to capture the preferential flow paths influenced by small-scale topographic features or man-made structures (river channels, buildings, roads, defences, etc.). Without this, the smoothing effect of mesh coarsening upon input topographical data in urban areas leads to a uncertain prediction of the inundation extent and the timing of inundation due to the simplified wetting process. Flow connectivity formed by the river channels and the road network, which has a strong control on urban floodplain hydraulics, is also better represented by mixing grid resolutions. Considering the large consequences in terms of economic losses caused by urban flooding, here we develop a GPU-accelerated non-uniform sub-/super-grid channel model (river channels with width below or above the fine grid resolution) for accurate and efficient urban flood modelling. Urban areas and the river channel network are forced to keep fine resolution, while a coarse representation, depending on the terrain gradient, is allowed for rural regions. This model allows the utilization of available sub-/super-grid scale bathymetric information for 1D in-channel flow representation, and a 2D model for floodplain with variable grid resolution, minimising the computational costs and below water line data requirements in the river channel. Three tests are set up to validate the model performance, and the results show that modelling the urban area with fine resolution improved the model reliability and accuracy, and reduces computational cost in rural areas where a coarse grid may be used.

How to cite: Rong, Y., Bates, P., and Neal, J.: Accelerating urban flood modelling using a GPU-parallel non-uniform structured grid and sub-grid approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3239, https://doi.org/10.5194/egusphere-egu23-3239, 2023.

Are your properties located far enough from rivers, sea shorelines or water bodies? If the answer is yes, this does not mean that they are fully safe from flooding.

In an era governed by continuous climate instability and unstoppable expansion of cities, the exacerbation of hydrometeorological events is increasing the occurrence of pluvial floods. Pluvial flooding is induced by the combination of two factors: extreme precipitations and the incapability of the ground/drainage systems to effectively handle excessive rainwater.

In an urban environment, the runoff generated by localized and intense rainstorms may quickly inundate streets and buildings undermining the safety of people and assets. The characteristic of being hardly predictable has inspired the definition of pluvial flood as an ‘invisible hazard’ and the related damages and losses are increasingly weighing on the budget of municipalities and private citizens.

Looking at the upsetting climate projections, experts are resolute in developing comprehensive methodologies and strategies for flood risk assessment and management.

In this work, we present the attempt of accomplishing a high-resolution pluvial flood risk assessment at the city scale. The city of Differdange (Luxembourg's third largest city) is used as case study in which the extreme rainfall-related impacts and hazards are analyzed through the implementation of a fully coupled 1D/2D dual drainage model. This type of hydrodynamic model closely mimics the complexity of an urban landscape allowing to simulate all hydraulic phenomena occurring both on the surface and through the sewer network. Despite the digital accuracy of these models, they are rarely implemented due to the vast amount of detailed information required; which are often unavailable.

The implementation of the hydraulic model follows two main steps: the bi-dimensional discretization of the surface and the 1D modelling of the whole drainage network.

Nowadays, many countries provide open-access high-resolution digital elevation models of their territories (50 cm for Luxembourg) and up-to-date cadastral planimetries from which essential information for the 2D component are extrapolated. Ground data is enriched by land use/cover and soil maps for the estimation of roughness and infiltration parameters.

The drainage network contemplates all pipes carrying rainwater, meaning the newer storm-water system and the old combined sewer network. The geometric specifications required are size, shape, elevation, material of pipes, manholes and tanks. Important infrastructures, such as flooding barriers, have been systematically added to the model.

The fully-distributed hydrological engine allows operating the rainfall-runoff transformation on each cell of the domain and the exchange of water between the surface and drainage network occurs through the nodes of the network (storm drains and manholes).

The model’s outcomes allow for assessing the level of hazard to which each building is exposed, identifying the critical nodes within the drainage network, and proposing mitigation strategies.

Furthermore, these insights may help authorities to improve their warning systems and emergency plans.

How to cite: Tamagnone, P., Schumann, G., and Suttor, B.: Pluvial flooding in urbanscapes: a full-coupled flood modelling approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12397, https://doi.org/10.5194/egusphere-egu23-12397, 2023.

How to cite: Fagugli, G., Pignone, F., Masoero, A., Gabellani, S., Morra di Cella, U., Rossi, L., and Munaretto, F.: City of Pemba: development of an automatic prediction tools for pluvial hazard assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7583, https://doi.org/10.5194/egusphere-egu23-7583, 2023.

Flooding is one of the most prevalent and disruptive natural hazards that affect livelihoods worldwide, especially in lower-income countries where proper drainage and flood protection measures tend to be less developed. Floods have become more frequent and intense over the recent decades and are expected to worsen their negative impacts in the future. Managing flood risk requires the evaluation of potential flood hazards and their consequences. Hydrodynamic models are generally employed to predict flood hazards (inundation extent, depth, velocity, etc.). One of the major concerns in flood hazard mapping is selecting an appropriate model structure. This study examines the flood predictions by a one-dimensional (1-D) hydrodynamic model for two geomorphologically distinct river reaches, the Adyar River, Chennai, India, and the Brazos River, Texas, USA. The results are compared against the simulation results of a two-dimensional (2-D) hydrodynamic model. An open-source model, HEC-RAS, with both 1-D and 2-D modeling capabilities, is employed for flood inundation modeling. The inundation patterns predicted by the 1-D model are found to vary significantly in the case of the Brazos River compared to those for the Adyar river. The study suggests that the simulations of flood inundation extent and maximum flow depth are influenced by the 1-D modeling assumptions on flood plains in river reaches characterized by wide flood plains with complex local terrain variations. The 1-D model simulations are also found sensitive to the magnitude of the flood event with respect to the hydraulic capacity of the reach.

How to cite: Jesna, , Sm, B., and Kp, S.: Investigating the potential of a 1-D hydrodynamic model for flood inundation modeling and hazard mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-362, https://doi.org/10.5194/egusphere-egu23-362, 2023.

Flood mapping is an essential step in flood risk assessment to reduce losses. Through flood mapping, we can detect vulnerable areas, assess flood impacts and create mitigation plans.
From the literature, we have two consolidated approaches to delineating flood maps. The first one is the hydrologic-hydraulic approach. Its strength relies on the possibility of simulating scenarios for different probabilities of occurrence (return period scenarios), considering the physics of the phenomenon. At the same time, the weaknesses of this approach regard the required amount of input data and high computational costs. The second approach is the geomorphological one, which allows to delineate flood-prone areas directly from some topographic features derived from a Digital Terrain Model (DTM), i.e. elevation, distance to the channel, etc.. Thanks to the limited request of input data and its rapidity in terms of computational efficiency, this approach is particularly appealing for large scale analyses. However, the geomorphological approach does not allow for the delineation of flood maps for different return period scenarios; further, the output is strongly linked to the quality of the input DTM.
Here we propose a model that combines the two approaches to enable preliminary mapping of flood areas for different scenarios at the regional scale; the model is named RESCUE, laRgE SCale inUndation model. RESCUE takes advantage from coupling geomorphological analysis and simplified hydrologic-hydraulic modeling, providing simple and reliable large scales inundation estimates. Like geomorphological models, it requires few data in input and has a high computational efficiency; while like hydrological-hydraulic models, it is physically-based and linked to a return period scenario.
Noteworthy, RESCUE allows for parameter uncertainty estimation through Monte-Carlo analysis, leading to a probabilistic assessment of flooded areas. Here, we show the potentialities and limitations through two examples: The Paglia-Chiani River system, and Central Apennines District (Central Italy).

How to cite: Pavesi, L., Volpi, E., and Fiori, A.: RESCUE a new physically-based large scale flood model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1316, https://doi.org/10.5194/egusphere-egu23-1316, 2023.

Accurate extraction of high-resolution digital elevation models (DEMs) is critical for many flood-sensitive rivers regarding landform change monitoring, hydraulic modeling, sediment transport tracking, and evaluation of river channel morphodynamics. Multi-temporal repeat monitoring of flood-vulnerable rivers is crucial due to rapid alteration of morphological properties of in-channel landforms. Thus, in this study the three-dimensional (3D) DEMs of the study region were acquired by unmanned aerial vehicle (UAV) based surveys in order for continuous tracking of stream channel morphology for the rivers sensitive to floods. Repeated high-resolution topography of the Bogacay basin, Antalya, Turkey was obtained in this study by means of UAV-based Structure from Motion (SfM) photogrammetry. The acquired topography during two consecutive years allows analysis of the relations between the main geomorphic processes related to landform alterations and their role in sediment transfer. In conjunction with the flood simulation, the scour depths at bridge piles after a probable flood (Q500) were predicted by HEC-RAS software. The flood analysis was conducted for a maximum runoff of 2560 m³/s with a calculated return period of 500 years (Q500). The results of HEC-RAS flood and scour analyses indicated that a maximum scour depth of 2.49 m could be expected during a probable flood with a maximum water depth of 8.2 m measured from the scoured depth. This water level corresponded to a 0.46 m of submersion of the bridge deck since the pier height was 5.25 m and the maximum flood velocity was predicted as 5.7 m/s. The results indicated that the alterations in the river channel after an expected flood event, allowed reliable evaluation of riverbed morphodynamics, while verifying that UAV-SfM and Dem of Difference (DoD) are useful tools in geomorphological dynamic mapping and in change monitoring studies.

How to cite: Özcan, O. and Özcan, O.: UAV-based monitoring of river bed morphodynamics for multi-hazard vulnerability assessment of bridges, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1858, https://doi.org/10.5194/egusphere-egu23-1858, 2023.

As a consequence of climate change and rapid urbanization, floods have increased both in terms of intensity and frequency, impacting especially the less developed countries of the World, and particularly sub-Saharan Africa. In such contexts, reliable flood risk assessments are of primary importance to support local authorities and stakeholders in emergency management and planning, and in the definition of effective risk mitigation measures. Still, their implementation is often hampered by lack of suitable data and resources. The present study has the main objectives of presenting challenges and identified solutions of performing flood hazard and risk analysis for the Megaruma and Muaguide rivers in Cabo Delgado, the northern province of Mozambique and also the poorest one. The downstream paths of the rivers cross the districts of Mecufi and Metuge, rural areas covered by fields cultivated by inhabitants who live on subsistence agriculture. During the wet season, some of the villages are completely isolated, with no access to adequate health services due to the floods that periodically affect the local population and their activities. As for many developing countries, data scarcity was the first limiting factor for quantitative analysis; therefore, much effort was primarily invested into data research. The hydrologic and hydraulic modelling to determine the flood hazard in the areas rely on free or at least cheap, global data (rainfall, terrain elevation and soil cover), meeting the second requirement of low available budget. On the contrary, an intensive field survey was required to collect data on the vulnerability of exposed assets at the base of damage assessment. Particular attention was also paid in the choice of free softwares and modelling tools. The resulting approach and methods can be easily exported to similar contexts, enabling robust flood risk analyses in the support of sustainable development.

How to cite: Rrokaj, S., Corti, B., Cancelliere, G., Costa, A., Giovannini, A., Molinari, D., Paz Idarraga, C. D., Radice, A., and Rotaru, A. M.: Challenges for flood hazard and risk assessment in Mozambique: the case of Megaruma and Muaguide rivers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11448, https://doi.org/10.5194/egusphere-egu23-11448, 2023.

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 structures passing the levee;

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

(iii) sandy soils, which are characterized by high permeability. From previous experience, we have noticed that 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;

(iv) 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.

Thus, the idea is to test different innovative UAV-supported survey approaches on the same test area, in combination with local on-site surveys, to compare and combine the obtained results. Firstly, we would test the possibility of using vegetation maps as an indicator of weak sections of the embankment. Up to now, a first drone survey data has been performed and the obtained RGB orthophotos have been elaborated to determine the Green Red Vegetation Index (GRVI), in order to acquire a vegetation cover map of the embankment. The obtained data have been calibrated with on-site surveys conducted by vegetation experts. To facilitate the identification of reedbeds, the 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. Further characterization may be carried out in order to investigate the relationship between reedbeds and soil characteristics.

The final aim is 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., Picco, L., Ceccato, F., Cola, S., and Simonini, P.: Thermal imaging and vegetation detection through UAV survey for large scale hazard monitoring of river levee, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14829, https://doi.org/10.5194/egusphere-egu23-14829, 2023.

On a global scale, the frequency and magnitude of flooding are getting worse. Although hydrologists around the globe have developed sophisticated flood models, their performance, especially over mountainous regions, is not comprehensively understood. The situation is challenging for flood-affected low-income nations, as sufficient resources are needed to procure commercial flood models with appropriate technical know-how. For instance, Bhutan, a mountain-dominated landscape in Asia, has been experiencing unprecedented flooding due to its fragile topography and climate change impacts. Unfortunately, a comprehensive data-driven modeling approach to determining flood hazard zones is missing in this region. The present study quantifies flood risks while considering a robust hydrodynamic flood model over Bhutan’s Chamkhar Chu River basin, a severely flood-prone area. The recently released open-source HEC-RAS v6.3 by the U.S. Army corps of Engineers, whose efficacy for flood inundation modeling is less explored, is considered to derive a set of flood risk maps. The coupled 1D-2D flood model setup is developed to simulate various flooding scenarios corresponding to design discharge and rainfalls for 50-yr, 100-yr, and 200-yrs. A corrected high-resolution Digital Elevation Model (DEM) from the ALOS-PALSAR product was utilized to reduce uncertainties in the final flood risk values. The simulated flood hazard maps for the settlements along the Chamkhar chu river are quantified in terms of flood depth, velocity, and a product of depth and velocity. A set of performance statistics are derived from testing the model performance while comparing the simulated inundation maps with the past inundation maps from MODIS satellite imagery. It was noticed that a significant portion of the central region is at a potential threat of very high flood risk as the simulated depth exceeds 3 m and velocities surpassing over 1.6 m/s. Such research will assist flood management agencies in prioritizing affordable structural and non-structural flood mitigation measures for the public that will reduce the impact of flood hazards in the future. Given the efficient computational performance of HEC-RAS v6.3 over a sensitive terrain, the study encourages the adoption of the model for accurately identifying flood risks over global mountainous regions for effective flood management.

How to cite: Namgyal, T., Mohanty, M. P., and Thakur, D. A.: How fitting are open-source flood models in capturing flood risks over mountainous regions: A prudent analysis over Chamkar Chu Basin, Bhutan using HEC-RAS v6.3, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11334, https://doi.org/10.5194/egusphere-egu23-11334, 2023.