Displays

Flood hazard maps are essential for development and assessment of flood risk management strategies. Conventionally, flood hazard assessment is based on deterministic approach which involves deriving inundation maps considering hydrologic and hydraulic models. A flood hydrograph corresponding to a specified return period is derived using a hydrologic model, which is then routed through flood plain of the study area to estimate water surface elevations and inundation extent with the aid of a hydraulic model. A more informative way of representing flood risk is through probabilistic hazard maps, which additionally provide information on the uncertainty associated with the extent of inundation. To arrive at a probabilistic flood hazard map, several flood hydrographs are generated, representing possible scenarios for flood events over a long period of time (e.g., 500 to 1000 years). Each of those hydrographs is routed through the flood plain and probability of inundation for all locations in the plain is estimated to derive the probabilistic flood hazard map. For gauged catchments, historical streamflow and/or rainfall data may be used to determine design flood hydrographs and the corresponding hazard maps using various strategies. In the case of ungauged catchments, however, there is a dearth of procedures for prediction of flood hazard maps. To address this, a novel multivariate regional frequency analysis (MRFA) approach is proposed. It involves (i) use of a newly proposed clustering methodology for regionalization of catchments, which accounts for uncertainty arising from ambiguity in choice of various potential clustering algorithms (which differ in underlying clustering strategies) and their initialization, (ii) fitting of a multivariate extremes model to information pooled from catchments in homogeneous region to generate synthetic flood hydrographs at ungauged target location(s), and (iii) routing of the hydrographs through the flood plain using LISFLOOD-FP model to derive probabilistic flood hazard map. The MRFA approach is designed to predict flood hydrograph related characteristics (peak flow, volume and duration of flood) at target locations in ungauged basins by considering watershed related characteristics as predictor/explanatory variables. An advantage of the proposed approach is its ability to account for uncertainty in catchment regionalization and dependency between all the flood hydrograph related characteristics reliably. Thus, the synthetic flood hydrographs generated in river basins appear more realistic depicting the observed dependence structure among flood hydrograph characteristics. The approach alleviates several uncertainties found in conventional methods (based on conceptual, probabilistic or geomorphological approaches) which affect estimation of flood hazard. Potential of the proposed approach is demonstrated through a case study on catchments in Mahanadi river basin of India, which extends over 141,600 km² and is frequently prone to floods. Comparison is shown between flood hazard map obtained based on true at-site data and that derived based on the proposed MRFA approach by considering the respective sites to be pseudo-ungauged. Coefficient of correlation and root mean squared error considered for performance evaluation indicated that the proposed approach is promising.

How to cite: Kezhkepurath Gangadhara, K. and Venkata Vemavarapu, S.: Probabilistic Flood Hazard Maps at Ungauged Locations Using Multivariate Extreme Values Approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-732, https://doi.org/10.5194/egusphere-egu2020-732, 2020.

Increasing flood risk was caused by expanding climate change. The floods directly or indirectly disrupt the railway system and arise a significant negative impact on our social-economic system. This study developed an integrated approach to explore the impact of large-scale future floods on railway system. Firstly, A three layered traffic flow simulation model was constructed to study propagation and amplification effects of component failure after the event of flooding in the system. Secondly, future runoff scenarios were produced by using five global climate models and three different representative concentration pathways. The future floods was simulated by using CaMa-Flood model after inputting future runoff scenarios. Furthermore, we imposing simulated future floods into traffic simulation system and develop two measurements to evaluate the impact of floods on the railway system as the perspective of the entire system. Here we explore the impact of floods on the real-world highway network of China. The results illustrate that: (i) Unprecedented uncertainty. The damage of the flood to the railway system is not linearly and positively correlated with representative concentration pathway and the year within different global climate models; Floods in different years have different impacts in connections among regions; (ii) Unacceptable damage. 59.76 % of railway segments inundated and 98.61461% of large cities could not be reached by extreme floods. These results have critical policy implications for the transport sector in reference to railway location and design, railway network optimization and protection and can be also easily adapted to analyze other spatial damages for valuable protection suggestions.

How to cite: Wang, W., Yang, S., Sun, H., Wu, J., and Gao, J.: Impact of large-scale future floods on the railway system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6376, https://doi.org/10.5194/egusphere-egu2020-6376, 2020.

Catastrophic flooding resulting from extreme tropical cyclones has occurred more frequently and drawn great attention in recent years in China. Coastal cities are particularly vulnerable to flood under multivariable conditions, such as heavy precipitation, high sea levels, and storms surge. In coastal areas, floods caused by rainstorms and storm surges have been one of the most costly and devastating natural hazards in coastal regions. Extreme precipitation and storm tide are both inducing factors of flooding and therefore their joint probability would be critical to determine the flooding risk. Usually, extreme events such as tidal level, storm surges, precipitation occur jointly, leading to compound flood events with significantly higher hazards compared to the sum of the single extreme events. The purpose of this study is to improve our understanding of multiple drivers to compound flooding in shanghai. The Wind Enhance Scheme (WES) model characterized by Holland model is devised to generate wind "spiderweb" both for historical (1949-2018) and future (2031-2060, 2069-2098) tropical cyclones. The tidal level and storm surge model based on Delft3D-FLOW is employed with an unstructured grid to simulate the change of water level. For precipitation, maximum value between tropical cyclone events is selected. Following this, multivariate Copula model would be employed to compare the change of joint probability between tidal level, storm surge and heavy precipitation under climate change, taking into account sea-level rise and land subsidence. Finally, the impact of tropical cyclone on the joint risk of tidal, storm surge and heavy precipitation is investigated. 

How to cite: Xu, H.: Compound impact of rainfall, tidal level and storm surge on flood risk from tropical cyclones in the coastal area of shanghai, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6518, https://doi.org/10.5194/egusphere-egu2020-6518, 2020.

Flood risk assessment strongly relies on accurate and reliable estimation of monetary flood loss. Conventionally, this involves univariable deterministic stage-damage functions. Recent advancements in the field promote the use of multivariable probabilistic loss estimation models which consider damage controlling variables beyond inundation depth. Although companies contribute significantly to total loss figures, multivariable probabilistic modeling approaches for companies are lacking. Scarce data and heterogeneity among companies impedes the development of novel company flood loss models.

We present three multivariable flood loss estimation models for companies that intrinsically quantify prediction uncertainty. Based on object-level loss data (n=1306), we comparatively evaluate the predictive performance of Bayesian networks, Bayesian regression and random forest in relation to established stage-damage functions. The company loss data stems from four post-event surveys after major floods in Germany between 2002 and 2013 and comprises information on flood intensity, company characteristics and private precaution. We examine the performance of the candidate models separately for losses to building, equipment, and goods and stock. Plausibility checks show that the multivariable models are able to identify and reproduce essential relationships of the flood damage processes from the data. The comparison of the prediction capacity reveals that the proposed models outperform stage-damage functions clearly while differences among the multivariable models are small. Even though the presented models improve the accuracy of loss predictions, wide predictive distributions underline the necessity for the quantification of predictive uncertainty. This applies particularly to companies, for which the heterogeneity and variation in the loss data are more pronounced than for private households. Due to their probabilistic nature, the presented multivariable models contribute towards a transparent treatment of uncertainties in flood risk assessment.

How to cite: Schoppa, L., Sieg, T., Vogel, K., Zöller, G., and Kreibich, H.: Probabilistic Flood Loss Models for Companies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7334, https://doi.org/10.5194/egusphere-egu2020-7334, 2020.

Catastrophe models are well established tools, traditionally used by the re/insurance industry to assess the financial risk to insured property (“exposure”) associated with natural perils. Catastrophe modelling is challenging, particularly for flood perils over large geographical scales, for a number of reasons. To adequately capture the fine spatial variability of flood depth, a flood catastrophe model must be of high spatial resolution. To validly compare estimates of risk obtained from catastrophe models for different geographical regions, those models must be built from geographically consistent data. To compare estimates of risk between any given collection of geographical regions globally, global coverage is required.

Traditional catastrophe models struggle to meet these requirements; compromises are made, often for performance reasons.  In addition, traditional models are typically static datasets, built in a discrete process prior to their use in exposure risk assessment. Scientific assumptions are therefore deeply embedded; there is little scope for the end user to adjust the model based on their own scientific knowledge.

This research presents a new and better approach to catastrophe modelling that addresses these challenges and, in doing so, has allowed creation of the world’s first global flood catastrophe model.

JBA’s Global Flood Model is facilitated by a technological breakthrough in the form of JBA’s FLY Technology. The innovations encoded in FLY have enabled JBA to create a model capable of consistent global probabilistic flood risk assessment, operating at 30m resolution and supported by a catalogue of 15 million distinct flood events (both river and surface water). FLY brings a model to life dynamically, from raw flood hazard data, simultaneously addressing the user configurability and performance challenges.

Global Flood Model and FLY Technology will be of interest to those involved in financial, economic or humanitarian risk assessment, particularly in and between countries and regions not covered by flood catastrophe models to date. The detail of how they work will be covered here, and their power in facilitating consistent global flood risk assessment will be demonstrated.

How to cite: Dunning, P., Styles, K., Evans, D., and Hutchings, S.: Global Flood Model: Revolutionising Flood Catastrophe Modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7632, https://doi.org/10.5194/egusphere-egu2020-7632, 2020.

Flooding events pose an ever increasing threat in a warming world. Safety standards for buildings and infrastructure are often based on past observations of local sea level, as measured by tide gauges and remote sensing systems. However, sea level at a given location is not an isolated property and is determined by a combination of factors. For extreme sea level events, there are two factors that of particular importance: the astronomical tide, and storm surges. In this work, we analysed measurements from 21 stations in the Norwegian tide gauge network, disentangling the factors contributing to the previously observed extreme events.

By separating the observed sea level into a tidal component and a storm surge component, we found that in many cases the observed extreme sea level events were caused by an extreme storm surge coinciding with only a moderate tide, or an extreme tide coinciding with only a moderate storm surge. This raises the possibility of a ‘super-flooding’ event, where an extreme storm surge may occur with an extreme tide. Even in the short records examined in this study (less than 40 years), the combination of the highest observed tide with the highest observed storm surge would greatly exceed in the 1000-year return level event at many locations. This is often used as a national standard for critical infrastructure.  

We further complement the work by analysing the storm tracks close to Norway. By relating the storm surges with the individual storms giving rise to them, we found that many storm surges during extreme sea level events were related to cyclones of only moderate intensity. Combined with the previous findings, this work suggests the need to assess extreme sea level return values for future construction and infrastructure planning as the result of a multi-variable system. This is in contrast to basing such assessments on the single variable of observed sea level as it is done today.

How to cite: Outten, S., Wolf, T., Mangini, F., Chen, L., and Nilsen, J. E.: Re-assessing extreme sea level events through interplay of tides and storm surges, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8000, https://doi.org/10.5194/egusphere-egu2020-8000, 2020.

Two case studies are considered in the UK, where uncertainty and drivers of coastal flood risk are explored through modelling and visualisations. Visualising the impact of uncertainty is a useful way of explaining the potential range of predicted or simulated flood risk to both expert and non-expert stakeholders.

Significant flooding occurred in December 2013 and January 2017 at Hornsea on the UK East Coast, where storm surge levels and waves overtopped the town’s coastal defences. Uncertainty in the potential coastal flooding is visualised at Hornsea due to the range of uncertainty in the 100-year return period water level and in the calculated overtopping due to 3 m waves at the defences. The range of uncertainty in the simulated flooding is visualised through flood maps, where various combinations of the uncertainties decrease or increase the simulated inundated area by 58% and 82% respectively.

Located at the mouth of the Mersey Estuary and facing the Irish Sea, New Brighton is affected by a large tidal range with potential storm surge and large waves. Uncertainty in the coastal flooding at the 100-year return period due to the combination of water levels and waves is explored through Monte-Carlo analysis and hydrodynamic modelling. Visualisation through flood maps shows that the inundation extent at New Brighton varies significantly for combined wave and surge events with a joint probability of 100 years, where the total flooded area ranges from 0 m² to 10,300 m². Waves are an important flood mechanism at New Brighton but are dependent on high water levels to impact the coastal defences and reduce the effective freeboard. The combination of waves and high-water levels at this return level not only determine the magnitude of the flood extent but also the spatial characteristics of the risk, whereby flooding of residential properties is dominated by overflow from high water levels, and commercial and leisure properties are affected by large waves that occur when the water level is relatively high at the defences.

How to cite: Maskell, J.: Uncertainty in coastal flooding: modelling and visualisation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8390, https://doi.org/10.5194/egusphere-egu2020-8390, 2020.

The understanding of the multifaceted nature of flood risk management (FRM) of a country requires the consideration of both social, technical as well as governance aspects. The inclusion of these components in the analysis and assessment of FRM allows comprehending the veracity of its interdependencies, its strengths and weakness that would, in turn, aid in improving the current system.

This paper presents an inter and transdisciplinary and participatory multi-method participatory approach to promptly assess Ghana’s current FRM practices, describing the current gaps and opportunities for improving FRM. Here, we describe the challenges on its institutional, governance and implementation, scientific, technical and social capacity levels and potential ways forward. The methodological  approach comprised a systematic literature review of 53 peer-reviewed articles, stakeholder analysis, engagement of stakeholders on workshops through focus group discussion and collaborative mapping, interviews with key individual stakeholders, and household surveys with 1,479 citizens living in flood prone areas. The stakeholders were identified and categorized into governance and implementation, academia and research and security agencies.

Results show that stakeholders have diverse and even contradictory views regarding FRM in Ghana. Overall, the findings indicate that: (1) the most critical regions are Accra, Kumasi, and the White Volta river basin, (2) the most crucial aspects for reducing vulnerability and exposure are related with high population density, social hotspots and location of Critical Infrastructure, (3) FRM  are unsustainable and unintegrated and it heavily relies on short-term projects and external funders, (4) reliable data is scarcily available and communities need to be engage more in the planning and provision of information and data, (5) there are weaknesses in flood early warning systems (FEWS), institutional collaborations, human capacity, trained FRM professionals and problems in policy implementation, (6) the most important vulnerability criteria are the existence of FEWS, disaster relief agencies, areas with a high density of children and poverty rate, (7) the interviewed communities in Accra and Kumasi claimed that flood disasters are caused mainly by human activities and interventions.

The applied participatory multi-method approach proved to be useful to capture the factual situation of the FRM in Ghana, this was shown when cross-referencing the results of the different methods. The use of a participatory and inter and transdisciplinary approach allowed capturing a multitude of views as well as the stakeholders needs and requirements in terms of FRM. The co-production of knowledged allowed improving the credibility, salience and legitimacy of project outputs.

How to cite: Almoradie, A., Madruga de Brito, M., and Evers, M.: An inter and transdisciplinary participatory approach to assess the current flood risk management practices in Ghana, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10879, https://doi.org/10.5194/egusphere-egu2020-10879, 2020.

In hydrodynamic modelling, the mesh resolution has a strong impact on run time and result accuracy. Coarser meshes allow faster simulations but often at the cost of accuracy. Conversely, finer meshes offer a better description of complex geometries but require much longer computational time, which makes their use at a large scale challenging. In this context, we aim to assess the potential of a two-dimensional shallow water model with depth-dependant porosity (SW2D-DDP) for flood simulations at a large scale. This modelling approach relies on nesting a sub-grid mesh containing high-resolution topographic and bathymetric data within each computational cell via a so-called depth-dependant storage porosity. It enables therefore faster simulations on rather coarse grids while preserving small-scale topography information. The July 2007 flood event in the Severn River basin (UK) is used as a test case, for which hydrometric measurements and spatial data are available for evaluation. A sensitivity analysis is carried out to investigate the porosity influence on the model performance in comparison with other classical parameters such as boundary conditions.

How to cite: Ayoub, V., Delenne, C., Matgen, P., Finaud-Guyot, P., and Hostache, R.: Towards fast large-scale flood simulations using 2D Shallow water modelling with depth-dependant porosity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11562, https://doi.org/10.5194/egusphere-egu2020-11562, 2020.

River floods can cause the destabilization of vehicles and vehicles can increase the negative impacts of floods when they are mobilized by the flow, causing economic and life losses. Because of this, integral flood management requires the identification and assessment of the risk to which vehicles are subjected at the crossing points between water currents and roads. In the present investigation a methodology was developed to calculate this risk based on the characteristics of vehicles, floods and traffic. The risk at each stream crossing is calculated by means of the statistical integral of the vehicle vulnerability given the actual exposition and hazard.

Hazard corresponds to the probability that flow causes the destabilization of each type of car and is determined from the hydrodynamic characteristics of the floods and the implementation of a stability criterion for partially submerged cars, through which a hazard index is established. Hazard is obtained through the combination of the probability that the flood event occurs with the values that the hazard index would take. The vulnerability of a given type of car is determined by means of a damage function defined from the values of the hazard index. The exposure is established based on the traffic characteristics and the driver behavior.

The methodology developed was applied in the municipality of Godelleta (Spain), finding that in approximately a quarter of the 25 intersections between streams and roads, the risk of vehicles due to flooding is relatively high, since it exceeds 0.2 vehicles per year. In approximately half of the intersections the risk is relatively low since it is less than 0.1 vehicles per year. Additionally, it was found that the risk of vehicles in stream crossings due to flooding is highly sensitive to the magnitude of the water level from which drivers decide to interrupt vehicle traffic through flooded crossing. The magnitude of the risk grows as drivers assume less conservative behavior, that is, when they decide to drive with higher water levels.

Key words

Risk of vehicles due to floods

Stability of cars partially submerged

Vulnerability of vehicles to floods

How to cite: Bocanegra Vinasco, R. A. and Francés, F.: Assessment of the risk of destabilization of vehicles at crossing points between streams and roads, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12405, https://doi.org/10.5194/egusphere-egu2020-12405, 2020.

Hydraulic models are increasingly used to assess the flooding hazard. However, all numerical models are affected by uncertainties, related to model parameters, which can be quantified through Uncertainty Quantification (UQ) and Global Sensitivity Analysis (GSA). In traditional methods of UQ and GSA, the input parameters of the numerical models are considered to be independent which is actually rarely the case. The objective of this work is to proceed with UQ and GSA methods considering dependent inputs and comparing different methodologies. At our knowledge, there is no such application in the field of 2D hydraulic modelling.

At first the uncertain parameters of the hydraulic model are classified in groups of dependent parameters. Within this aim, it is then necessary to define the copulas that better represent these groups. Finally UQ and GSA based on copulas are performed. The proposed methodology is applied to the large scale 2D hydraulic model of the Loire River. However, as the model computation is high time-consuming, we used a meta-model instead of the initial model. We compared the results coming from the traditional methods of UQ and GSA (i.e. without taking into account the dependencies between inputs) and the ones coming from the new methods based on copulas. The results show that the dependence between inputs should not always be neglected in UQ and GSA.

How to cite: Pheulpin, L. and Bacchi, V.: Uncertainty quantification and global sensitivity analysis with dependent inputs: Application to the 2D hydraulic model of the Loire River, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18939, https://doi.org/10.5194/egusphere-egu2020-18939, 2020.

The Barotse floodplain in the Western Province of Zambia, is a major feature of the Upper Zambezi River, covering an area of 11,000km², and is inundated annually by a flood cycle that ranges from minimum values in September, to peak levels in April. The annual flooding of the area provides a number of challenges, and critically is a significant component of the life cycle of mosquitos, the principle vector for the transmission of malaria. A research project, FLOODMAL, has been developed to apply process based modelling approaches to the life cycle of the mosquito in the floodplain. A significant component of this approach is the development of a 1D-2D model which can be used to predict the formation of water bodies that are essential to the mosquito breeding cycle. This research presents the uncertainties associated with developing the flood model, with an emphasis on model performance through simulation time. In a typical model exercise, the calibration of input parameters are associated with ensuring that model performance is optimised for representing the peak of a flood event. This can be at the cost of providing a consistent level of model performance throughout a simulation, which is essential in this research.

Using the LISFLOOD-FP computer code, and TanDEM-X1 terrain data, a baseline model of the Barotse floodplain was developed for the 2009 and 2018 events. A set of initial model runs identified key processes to be represented in the model, including evaporation and infiltration. The calibration of the model was focused on defining parameters for surface roughness, channel roughness, evaporation, infiltration, and defining channel topography. A number of datasets were available for model calibration, such as LandSAT imagery to compare observed and modelled extent at various points throughout the year, and downstream river gauge data. To further understand the uncertainties associated with the modelling, sensitivity analysis was undertaken using an emulator- based approach to define the contribution of the input parameters to overall model variance. The results indicate that parameters that control the movement of water across the floodplain (surface roughness) are generally the most significant of the inputs at all points in the year, although the level of this significance changes at different phases.

How to cite: Willis, T., Smith, M., Cross, D., Hardy, A., Ettritch, G., Malawo, H., Sinkombo, M., Chalo, C., and Thomas, C.: Uncertainty in the modelling of large scale flood events in the Barotse floodplain, Zambia , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19000, https://doi.org/10.5194/egusphere-egu2020-19000, 2020.

Flash floods, as a result of frequent torrential rainfalls caused by tropical storms, thunderstorms,
and hurricanes, are a prevalent natural disaster in the southeast U.S. (SEUS), which frequently
threaten human lives and properties in the region. According to the U.S. National Weather
Service (NWS), flash floods generally initiate within less than six hours of an intense rainfall
onset. Therefore, there is a limited chance for effective and timely decision-making. Due to the
rapid onset of flash floods, they are costly events, such that only during 1996 to 2017 flash
floods imposed 7.5 billion dollars property damage to the SEUS. Therefore, estimating the
potential economic damages as a result of flash floods are crucial for flood risk management and
financial appraisals for decision makers. A multitude of studies have focused on flood damage
modeling, few of which investigated the issue on a large domain. Here, we propose a systematic
framework that considers a variety of factors that explain different risk components (i.e., hazard,
vulnerability, and exposure) and leverages Machine Learning (ML) for flood damage prediction.
Over 14,000 flash flood events during 1996 to 2017 were assessed to analyze their characteristics
including frequency, duration, and intensity. Also, different data sources were utilized to derive
information related to each event. The most influential features are then selected using a multi
criteria variable selection approach. Then, the ML model is implemented for not only binary
classification of damage (i.e., whether a flash flood event caused any damage or not), but also for
developing a model to predict the financial consequences associated with flash flood events. The
results indicate a high accuracy for the classifier, significant correlation and relatively low bias
between the predicted and observed property damages showing the effectiveness of proposed
methodology for flash flood damage modeling applicable to variety of flood prone regions.

How to cite: Alipour, A., Abbaszadeh, P., Ahmadalipour, A., and Moradkhani, H.: From Flash Flood Vulnerability and Risk Assessment to Property Damage Prediction: the Value of Machine Learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19057, https://doi.org/10.5194/egusphere-egu2020-19057, 2020.

Computational fluid dynamics (CFD)  for “typical Dutch” houses failure: experiments and numerical modelling comparison.

Authors: Andres Diaz Loaiza¹, Benedikt Bratz^1,2, Jeremy Bricker¹ and Paul Korswagen¹

1- Hydraulic Structures and Flood Risk, Technical University of Delft, 1- Technische Universität Braunschweig

Coastal and riverine floods can be a catastrophic natural hazard with importance consequences. Many of the casualties occurring during these events can be attributed to the collapse of residential houses, and it is thus required to gain knowledge about the failure mechanism of these structures. Multiple variables can lead to various flow conditions that will in turn represent different load pressures over the house; among these, the type of the material (used in the construction), the orientation angle in respect to the main flow direction, the shape of the structure, and the urban density (blockage ratio), are relevant. In the present paper, small scale experiments are compared with CFD simulations performed with openFOAM in order to obtain a numerical model than can predict different combinations of load pressures for various flood events.

The present study aims to represent different “typical Dutch” houses near or close to a dam break in which rapid high flow velocities and depths can be presented. The flow conditions and load pressures outputs are compared to physical results in order to validate the numerical model.

How to cite: Díaz Loaiza, M. A., Bratz, B., Bricker, J., and Korswagen, P.: Computational fluid dynamics (CFD) for “typical Dutch” houses failure: experiments and numerical modelling comparison., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21735, https://doi.org/10.5194/egusphere-egu2020-21735, 2020.

Flood damages caused by abnormal climate changes occur frequently every year. Systems to predict and respond to disasters are required to prepare for flood damages. The embankment overflow and collapse mechanism due to the rapid increase of river water level in flood are quite complex, varied, and uncertain. In this study, changes of river embankment collapse widths and flood inflows were calculated. In this case, the MCS-based probability flood levels were used based on th hydrologcal scenario, which takes into account the uncertainty of the parameters of extreme precipitation through the abnormal frequency analysis. In addition, two-dimensional inundation analysis was performed to estimate flood depth and flood area, and to produce a probabilistic flood hazard map. By quantitatively evaluating the uncertainty of the parameters in consideration of the overall mechanism of flood occurrence, we obtained more reliable predictions of flood depth than conventional deterministic analyses.

How to cite: Lee, C., Nam, M. J., and Lee, J. Y.: Development of Flood Hazard Map from Probabilistic Embankment Collapse Inflow, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3200, https://doi.org/10.5194/egusphere-egu2020-3200, 2020.

The purpose of this work is the implementation and the application of a semi-automatic procedure for modelling flood events, based on the coupled use of a GIS subroutine and a two-dimensional (2D) lattice Boltzmann hydraulic model solving shallow water equations. The lattice Boltzmann method (LBM) with cumulant collision operator is chosen as a numerical technique for the solution of the hydrodynamic problem. The cumulant LBM is based on the use of cumulants as basis and relaxes, in the collision step, quantities (cumulants) that are Galilean invariant by construction. It overcomes the defects in Galilean invariance of the original multi relaxation times methods and it has been shown to further improve stability. An adaptation of the original formulation for a single-phase fluid is therefore proposed and developed to reproduce shallow free surface flows. Special attention is due to the wet-dry front in shallow flows; in fact, a correct simulation of such processes plays a crucial role in practical engineering studies.

The chosen mesoscopic model, thanks to the peculiar characteristic of LBM codes of being easily parallelized, could allow accurate and realistic wave prediction in a low computation time, introducing the possible application for the assessment of the hydraulic risk.

The preparation of the input data (pre-processing) and the analysis of the modelling results (post-processing) are assisted by an interchange routines using an open source GIS platform.

The proposed methodologies are tested and validated through the use of analytical solutions and experimental solutions. Moreover, the suitability of the proposed mathematical model for large scale hydraulic engineering applications is discussed through the modelling of a real flood event, highlighting the good performances of the cumulant model.

How to cite: Di Francesco, S., Venturi, S., and Geier, M.: Cumulant lattice Boltzmann approach: an application to hydraulic risk, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5416, https://doi.org/10.5194/egusphere-egu2020-5416, 2020.

Flooding is the number one natural disaster in terms of insured and uninsured losses annually. The development of reliable methods for flood simulation have greatly improved our ability to predict floods thereby reducing damages and loss of life in flood-prone regions. However, there is still a lot of room for improvement and innovation to provide better predictions, especially for flash floods, particularly in urban areas  This is addressed in the present study, the goal of which it is to improve simulation and prediction of flash floods and to develop a spatial decision-making model for implementing flood protection measures. In this regard, different approaches for flood simulation and flood protection should be applied. The proposed methodology links flood hazard modeling, remote sensing and machine learning methods. Combining these physical models and data driven methods will result in a more reliable hybrid model that can be employed for prediction of (flash) floods and event analysis. In order to achieve the research goal of present study we: i) add more functionality to a hydrodynamic model code; ii) complement the latter with data driven methods ;iii) develop a spatial decision-making model framework for defining flood protection measures, iv) validate process-based and data driven methods, and finally v) cross-evaluate Light Detection And Radar (LiDAR) topography with available local super-resolution drone data to assess the ability to incorporate local flood defenses into the models. The most important outcome is the creation of valuable flood maps in areas where it matters - while accounting for effects of land use and climate change. This will serve scientists as well as land and risk management authorities with better actionable flood risk information in locations where people and assets are located and in danger. It also develops innovative methodologies for estimating the changing risk from flash floods based on land use scenarios and climate change projections. Moreover, developing spatial multi-criteria decision making (SMCDM) can help decision makers to determine suitable locations and methods for flood protection measures. These methods will be particularly valuable in the context of solving current challenges of accounting for and mitigating flash floods and the effects of climate change.

How to cite: Zare, M., Schumann, G., Teferle, F. N., Matgen, P., and Bates, P. D.: Presenting hydrological and data driven flood simulation-prediction methods to develop a decision-making model , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13319, https://doi.org/10.5194/egusphere-egu2020-13319, 2020.

Godunov-type schemes are widely applied to solve shallow water equations. In this study, a novel non-negative water depth Multislope MUSCL reconstruction method is incorporated into a two-dimensional unstructured cell-centered Godunov-type finite volume model to simulate shallow water flows, It is verified that the method performs well in avoiding non-physical oscillation and also has well-balanced performance by simulate three test cases. Due to the limitation of CFL conditions, mesh refinement will greatly increase the computational cost. In this study, A Local Time Stepping(LTS) strategy is specifically designed to greatly improve the computational efficiency. In addition, in order to make the model suitable for more application scenarios, we have realized the coupling of one-dimensional and two-dimensional models. Based on the above three improvements, we have developed a stable and efficient flood routing model.

How to cite: Zhou, S. and Hu, H.: A Stable and Efficient Flood Routing Model Based on Unstructured Grid, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13387, https://doi.org/10.5194/egusphere-egu2020-13387, 2020.

A high-resolution Digital Terrain Model 5m x 5m, land use characteristics and a validated output hydrograph from an extreme rainfall event were used as input to the coupled 1D/2D HEC-RAS hydraulic model in order to obtain the flooded area extent at the downstream segment of a small basin in the island of Crete. A spatially varying Manning’s roughness coefficient n was used to identify the differences between land coverage for the channel bed and the floodplain. Lateral structures were designed along the left and right overbanks of the stream, connecting the 1D stream flow with the 2D flow areas. The weir coefficient, used to convey the flow above the lateral structures, was also chosen for model validation in the control cross section. Detailed flood hazard mapping at the peak discharge was produced, along with the flood depths at times before and after the heavy precipitation event, in order to obtain the time evolution of the flooded area extent. The results obtained by the 1D hydraulic model are limited in their 2D lateral output that is crucial to the floodplain extent. The 1D/2D provides more detailed output concerning the flood extent at the peak discharge, as well as the maximum water depths and velocities at every grid point of the computed mesh. Defining accurate flood inundated areas is of utmost importance in civil protection agencies in order to initiate a proper early flood warning. At the same time, each EU Member State country is required to produce flood hazard maps according to EU Floods Directive at the river basin level. These 1D/2D simulation results can be beneficial in the aforementioned requirements for low probability extreme floods’ basin management.

How to cite: Sarchani, S. and Tsanis, I.: Modelling the flooded area extent at the downstream segment of a small basin through a coupled 1D/2D hydraulic model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13977, https://doi.org/10.5194/egusphere-egu2020-13977, 2020.

Flash Floods cause significant material and human damage worldwide. In France, they frequently hit small rivers of the Mediterranean area, often inducing catastrophic consequences.

Considering the large number of possibly affected small watercourses, the use of automated flood-mapping methods may be of great help for the identification of the possibly affected areas and the prediction of the potential consequences of this type of floods.

In 2019, a first evaluation of three automated inundation-mapping methods, directly implemented on high-resolution Digital Terrain Models (DTM) was presented (https://meetingorganizer.copernicus.org/EGU2019/EGU2019-15710-1.pdf). The automatically retrieved flood extent maps were compared with simulated reference maps from local expert studies.        

As a continuation of this work, an application of the two best performing of these methods (1D caRtino approach and 2D Floodos approach), is presented here for the simulation of  three recent flash flood events:

• The 15^th of June 2010 flood on the Argens watershed: 25 deaths, more than 1 billion € of economic damage, 585 km of affected and simulated rivers.
• The 3^rd – 4^th, of october 2015 floods in the French Riviera: 20 deaths, and 600 million € of economic damage, 131 km of affected and simulated rivers.
• The 15^th - 16^th of October 2018 flood on the Aude watershed: 15 deaths, approximatively 300 million € of economic damage, 569 km of affected and simulated rivers.

At first, the peak discharges for each reach of the stream network are estimated with a hydrological model (CINECAR), calibrated against discharge values based on extensive post-event surveys. The hydraulic simulations with the two methods are then run for each reach separately in steady-state regime, based on estimated peak discharges, to obtain simulated flood maps at the reach scale that are then combined to obtain a flood extent map for the simulated event. The computation times are calculated for the two methods and compared.

The simulation results are compared with observed flood extent maps and high water marks. The flood extent maps are compared based on a critical success index criterion (CSI), showing an overall very good correspondence. The simulated water levels show a difference of less than 50 cm with high water marks in most cases.

Finally, a sensitivity analysis to the quality of DTM input information and roughness coefficients is presented.

How to cite: Hocini, N., Gaume, E., Payrastre, O., Bourgin, F., Davy, P., Lague, D., Pons, F., and Poinsignon, L.: Evaluation of two automated inundation-mapping methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16441, https://doi.org/10.5194/egusphere-egu2020-16441, 2020.

In this work, a methodology for quantifying the relative impact of hydrological and hydraulic modelling parameterizations on uncertainty of inundation maps has been developed and applied in the Marta river basin, central Italy. A lumped rainfall-runoff forced by a synthetic hyetograph derived from regionalized IDF curves and a Quasi-2D hydraulic model were adopted to delineate the flood hazard maps related to different return periods. The uncertainty related to the design rainfall estimation method, given by the limited length of the time series from which the IDF curves fitted, was considered adopting a Monte Carlo approach.  On the other hand, the uncertainty related to floodplain roughness was considered adopting literature values. The above mentioned methodologies for representing both uncertainties were applied simultaneously and separately. Results, expressed in terms of variability of simulated flood extents and flow depths, suggest a significant predominance of the uncertainty related to hydrological modelling as respect to the hydraulic modelling.

How to cite: Volpi, E., Annis, A., Nardi, F., and Fiori, A.: Is hydraulic modelling parametrization the major source of variability in flood hazard assessment? Insight into hydrologic uncertainty and the role of design rainfall in probabilistic flood maps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19385, https://doi.org/10.5194/egusphere-egu2020-19385, 2020.

Keeping ISA Modern is a project of Fondazione Politecnico di Milano and other partners aimed at planning the conservation of some of the buildings (Schools) of the University of Arts (ISA) of Cuba, built over a former country club, designed by eminent architects of the time (Vittorio Garatti, Roberto Gottardi and Ricardo Porro), and bestowed with the status of UNESCO World Heritage in 2003.

Most of the Schools are currently unusable, also due to damages caused by frequent floods from the surrounding Rio Quibù river, and they need urgent restoration if they are to be used. Personnel of Politecnico di Milano carried out a field survey on the Rio Quibù during 2019, and also based upon information from the Cuban National Institute of Hydraulic Resources (INRH) they studied established flood risk for ISA.

Here, we built a high-resolution digital terrain model (DTM) of the park where Schools are located, using laser scanner data, and previously georeferenced points. Using field measurements taken in June 2019 we were able to assess geometry (included bridges), slope and roughness coefficients of the main channel of the Quibù river, influence of the sea level. Then using as input critical discharge data provided by INRH we evaluated flood area and flood volume for 4 representative return periods (5, 20, 50, 100 years).

The most impacted building is the School of Ballet, located within a narrow meander of Rio Quibù, immediately upstream of a narrow bridge, clogging largely during floods, only 1 km far from the sea, and with drainage system unable to discharge storm water.

Given the high required cost, a partially collapsed wall originally partially protecting the School of Ballet was not rebuilt, and we are now exploring flood mitigation strategy which are cheaper, and feasible from the point of view of compatibility with the historical and architectural value of the building.

How to cite: Bignami, D. F., Stucchi, L., Bocchiola, D., Zecchin, C., Del Curto, D., Garzulino, A., and Rosso, R.: Flood risk assessment and cultural heritage impact in the Instituto Superior de Arte (ISA) in Habana., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20084, https://doi.org/10.5194/egusphere-egu2020-20084, 2020.

The FLORIS project aims to study innovative approaches for the development of integrated flood risk scenarios taking into consideration critical specific issues of areas at risk and the consequences of high frequency/low damage events that affect them. High frequency floods still involve and require mitigation actions on the part of civil protection and citizens before floodwaters inundate the land and directly impact assets. These emergency actions can benefit from enhanced protocol development based on realistic scenarios.

In particular, the main idea is to develop a supporting decision tool for the comparative analysis of disaster reduction strategies in flood risk management. This will have a specific focus on studying the functional vulnerability of critical infrastructure in order to preserve their efficiency during and after hazardous events. This include, hydraulic modelling at a finer scale, vulnerability and damage analysis at single element scale.

To address the project aims, identification of critical infrastructures that influences both the actions and outcomes of civil protection in flood prone areas and the disruption to the at-risk public, will be undertaken. To achieve the goal, initial steps consist of presenting to, and discussing with, civil protection teams the established approaches already available to them together with those identified by the project team from past research and within the literature. This will identify opportunities to further develop the civil protection protocols via innovative modelling of cascade effects incorporating existing algorithms. The developed procedures for flood risk reduction, taking into account resource management requirements will then be applied in a pilot case study, in the city of Berat, Albania and in Sarajevo, Bosnia and Herzegovina.

Working with the relevant professionals who are the principal beneficiaries of the project enables protocols to be co-developed to include associated physical, social and resource characteristics particular to the selected location. The main achievements will include enhanced management for flood protection in the beneficiary organisation with increased awareness of the interrelationships both spatially and temporally enhancing management protocols, protocols more closely aligned with existing beneficiaries’ procedures and resources for sustainability and establishing tools that are transferable to other regional and country contexts.

The main expected output is a suite of tools, embedded in a cascade procedure, able to support various actors (Civil Protection, municipalities, administrations, professionals, etc.) in planning and design measures to improve flood risk management actions under different and variable risk scenarios including climate and global change.

Acknowledgements

FLORIS (Innovative tools for improving FLood risk reductiOn stRategIeS) project has received funding from the EUROPEAN COMMISSION - under the 2018 Call Prevention and Preparedness in Civil Protection  (Project number: UCPM-2018-PP-AG  - 826561)

How to cite: Aronica, G. T., Brigandi, G., Binesh, N., McCarthy, S., Viavattene, C., Priest, S., Hadzcic, E., Deda, M., Rossello, L., and Koxhai, H.: Innovative tools for improving flood risk reduction strategies: the FLORIS project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20797, https://doi.org/10.5194/egusphere-egu2020-20797, 2020.

How to cite: Ventimiglia, U., Aronica, G. T., and Candela, A.: Flood proofing measures cost-efficiency analysis for hydraulic risk mitigation in an urbanized riverine area, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20508, https://doi.org/10.5194/egusphere-egu2020-20508, 2020.

Scaling the piping process

W.J. Dirkx*^a, L.P.H. van Beek^a, M.F.P. Bierkens^a,b

^{*Corresponding author}

^{a University of Utrecht, Department  of Physical Geography, Faculty of Geosciences, P.O. 80.115, 3508 TC,}    

  ^{Utrecht, the Netherlands}

^{bDepartmentStochastic hydrology and geohydrology, Deltares, P.O. 85467, 3508 AL, Utrecht, the Netherlands.}

Seepage underneath river embankments during high water events can lead to erosion by piping. Elevated hydraulic gradients will drive groundwater flow, which when large enough, may breach the confining layer by bursting and wash out finer non-cohesive sediments, especially if the outflow is concentrated in a single point. As material is removed, a pipe may form and continue to progress upstream eventually undermining the embankment. Although often approached as a geotechnical or engineering problem in terms of embankment failure, the process can also be approached from different scales as a geohydrological problem. On the scale of an entire delta there are multiple channel belts that define the regional groundwater flow patterns. On the scale of a single stretch of river embankment the interaction between the river, present channel belts, their orientation, and channel belt architectural elements dominate the exact location of bursting and associated discharge. From there on the process scale becomes important, where the grain size distribution within the facies where the piping is taking place. And the process is dominated by regional bulk hydraulic conductivity in terms of discharge magnitude and grain size distribution at the tip of the pipe in terms of erodibility. In this study, a set of embedded models for the various scales is developed and tested that simulates the formation of a single pipe at these various scales in a holistic approach. Geohydrological conditions are linked to a representation of saturated hydraulic conductivity based on the local grain size distribution to model the feedback between groundwater flow, subsurface conditions and piping at these various scales. Thus, the model assesses the influence of subsurface heterogeneity on piping and its performance was assessed on the basis of field observations and laboratory experiments. Our results show the validity of the model and stress the need to treat piping as a three-dimensional geohydrological problem.

How to cite: Dirkx, W.-J., Van Beek, R., and Bierkens, M.: Scaling the piping process, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20984, https://doi.org/10.5194/egusphere-egu2020-20984, 2020.

The delimitation of flood-prone areas is an important non-structural measure that proves to be effective in the long term in reducing food risk.

In Italy, more than 20 basin’s Units of Management (UoMs) were in charge to delineate the flood hazard zoning (FHZ) for three different flood return periods. Mostly, FHZ was prepared using physically based models i.e., considering the rainfall-runoff transformation and simulating the flood discharge through the river network. Physically-based models require many inputs and boundary conditions including: hydro-meteorological data, detailed characterization of the geometry of the riverbeds, roughness, infiltration parameters and also real hydrometric measurements in order to be calibrated. Physically based modelling is therefore a long, time consuming and resource intensive process that should be frequently updated to take into account the river channel changes. As a consequence, the Italian FHZ suffers from an underlying lack of homogeneity across the different UoMs, resulting in significant differences on the percentage of the river network for which the flood-prone areas were delineated.

As alternatives to physically based models, in recent years many authors have produced maps of flood susceptibility or hazard using expert (e.g. Analytic Hierarchy Process) or data-driven (e.g.  multivariate statistics or machine learning) approaches. Such methods were mostly used in ungauged territories where hydro-meteorological data is not available.

Here we present a procedure, named Flood-SHE (Flood - Statistical Hazard Evaluation), which is aimed at the delineation of flood-prone areas and the corresponding expected water depth, using a multivariate statistical classification model. Flood-SHE was applied to the entire Italian territory with the aim to integrate the UOMs FHZ where it is not available or incomplete. The classification model was trained exploiting the existing UoMs FHZ and using, as independent variables, a set of geomorphometric layers (derived at 10x10 meters ground resolution) which includes the distance and height to the closest rivers and to the basins outlets, the local DEM slope, a stream order classification criterion and the DEM local roughness. Random training and validation areas were used for the classification model in order to obtain an estimation of the uncertainty of the values of the predictive performance indexes. Results highlight (i) the significance of the the variables distance and height to the closest rivers, roughness and stream order in predicting the flood-prone areas, (ii) the impact of the UoMs morphology and the quality of UoMs FHZ on the reliability of the statistically modeled flood-prone areas.

How to cite: Marchesini, I., Rossi, M., Salvati, P., Donnini, M., Sterlacchini, S., and Guzzetti, F.: A data-driven statistical approach for flood hazard zoning at national scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21969, https://doi.org/10.5194/egusphere-egu2020-21969, 2020.

How to cite: Del Vecchio, M., Brizzi, S., De Michele, C., Menduni, G., and Pedone, M. A.: FLOOD SUSCEPTIBILITY in ENDORHEIC AREAS: The case study of Salento peninsula in Apulia (Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22470, https://doi.org/10.5194/egusphere-egu2020-22470, 2020.

How to cite: Menduni, G., Bignami, D., De Michele, C., Del Vecchio, M., and Harikumar, A.: Physically based metrics to evaluate the hydraulic distance between the drainage network and a DEM cell, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22471, https://doi.org/10.5194/egusphere-egu2020-22471, 2020.