Worldwide, the frequency and magnitude of extreme flooding are steadily increasing, causing considerable losses of life and property. It hampers well-being and economic growth in many countries, so that flood forecasting and flood risk assessment have become of upmost importance. New and rapidly developing techniques are becoming widespread, such as unmanned aerial vehicles (UAV), synthetic-aperture radar (SAR) or satellite-based systems. Combined with fit-for-purpose hydrodynamic models, these techniques pave the way for breakthroughs in flood assessment and flood risk management. This provides a unique platform for the scientific community to explore the driving mechanisms of flood risk and to build up efficient strategies for flood mitigation and enhancing flood resilience.
This session invites presentations on research based on high-resolution aerial and satellite techniques like UAV, SAR, Altimeter, SCATSAT-1, etc. for flood monitoring, including mapping of inundation extent, flow depths, velocity fields, flood-induced morphodynamics, debris transport. It also invites the presentation of innovative modelling techniques of flood hydrodynamics, flood hazard, damage and risk assessment, as well as flood relief prioritization, dam and dike(levees) break floods, and flood mitigation strategies. Studies dealing with the modelling uncertainties and modern techniques for model calibration and validation are particularly welcome.
Furthermore, real-time flood inundation mapping is critical aspect for the evacuation of people from low-lying areas and to reduce casualties. Acquisition of real-time data gained through UAV-based flood inundation mapping and modelling, as well as assessment of uncertainties in real-time aerial surveying are welcome in this session.

Invited speaker:
Frederik Kratzert.
Mr Kratzert holds a MSc in environmental engineering with focus on hydrology and is now doing a PhD in Machine Learning at the Johannes Kepler University, Linz, Austria under the supervision of Sepp Hochreiter. His research is focused around the use of the LSTM neural network for hydrological/environmental modeling and his PhD is funded by Google AI.

Co-organized by HS13
Convener: Dhruvesh Patel | Co-conveners: Cristina Prieto, Benjamin Dewals, Dawei Han
| Attendance Mon, 04 May, 08:30–10:15 (CEST)

Files for download

Session summary Download all presentations (33MB)

Chat time: Monday, 4 May 2020, 08:30–10:15

Chairperson: Dr. Dhruvesh +Dr. Cristina+Dr. Benjamin+Prof. Dawei Han
D1876 |
| Highlight
Max Steinhausen, Kai Schröter, Stefan Lüdtke, and Heidi Kreibich

Floods are the most costly natural disasters for European economies and expected to increase in frequency and magnitude within a changing climate. Governmental agencies, as well as the (re-)insurance sector, rely on accurate flood loss estimations on the European scale to support climate change adaptation policies, prepare for economic impacts, for instance, via the EU solidarity fund and calculate premiums.

Flood loss estimation on the European scale is currently based on deterministic depth-damage functions different for each country. This leads to a fragmented approach in flood loss estimation, greatly simplifying the representation of damage processes without information about associated uncertainties. To overcome these shortcomings we developed the Bayesian Network Flood Loss Estimation MOdel for the private sector (BN-FLEMOps). BN-FLEMOps estimates relative loss to residential buildings depending on flood experience of the population, precautionary measures, building area, building type, return period, duration and water depth (Wagenaar et al. 2018). The structure of this probabilistic multi-variable model is based on empirical data from post-flood surveys and uses consistent continent-wide proxy data for European scale application. BN-FLEMOps was successfully validated in three case studies in Italy, Austria and Germany. The officially reported loss figures of the past flood events were within the 95% quantile range of the probabilistic loss estimation (Lüdtke et al. 2019).

The probabilistic approach enables the quantification of uncertainties of the loss estimates. Model outputs are generated as loss distributions in high spatial resolution, offering Europe-wide information about risk and uncertainty. Thus, providing support for decision-making processes in flood risk management.

Easy applicability to the BN-FLEMOps model is ensured by its implementation in the standardized OASIS loss modeling framework (lmf). The OASIS lmf enables a plug and play combination with various input data sets and other models.

A first application of BN-FLEMOps for a Europe-wide 100 years flood hazard scenario provided by the Joint Research Center resulted in accumulated loss for residential buildings in Europe of 79.0 billion euro (Q20 = 32.3; Q80 = 213.8).



Lüdtke, S., Schröter, K., Steinhausen, M., Weise, L., Figueiredo, R., Kreibich, H. (2019 online first): A consistent approach for probabilistic residential flood loss modeling in Europe. - Water Resources Research. DOI: http://doi.org/10.1029/2019WR026213

Wagenaar, D., Lüdtke, S., Schröter, K., Bouwer, L. M., Kreibich, H. (2018): Regional and Temporal Transferability of Multivariable Flood Damage Models. - Water Resources Research, 54, 5, pp. 3688-3703. DOI: http://doi.org/10.1029/2017WR022233

How to cite: Steinhausen, M., Schröter, K., Lüdtke, S., and Kreibich, H.: Probabilistic flood loss estimation for residential buildings in Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20446, https://doi.org/10.5194/egusphere-egu2020-20446, 2020.

D1877 |
Attilio Castellarin, Simone Persiano, Caterina Samela, Andrea Magnini, Stefano Bagli, Paolo Mazzoli, and Valerio Luzzi

The steady increase of economic losses and social consequences caused by flood events in Europe, as a result of the combined effects of anthropization (e.g. land-use and land-cover changes) and climate change, calls for updated and efficient technologies for assessing pluvial, fluvial and coastal flood hazards and risks. In this context, the EIT-Climate KIC SaferPLACES () project aims at exploring and developing innovative and simplified modelling techniques to assess and map flood hazard and risk in urban environments under current and future climates. Concerning fluvial flooding, detailed inundation maps can be accurately obtained by means of hydrological and hydraulic numerical models, whose application, though, is often very resource intensive. For this reason, consistent and harmonized national flood hazard maps are still lacking in many countries of the world. Several studies have proved that flood-prone areas can be delineated by considering linear binary geomorphic classifiers, which are computed by analysing Digital Elevation Models, DEMs, and whose threshold values are calibrated relative to existing hydraulic flood hazard maps. One of these indices, the so-called Geomorphic Flood Index (GFI), was recently shown to be cost-effective, reliable and efficient for identifying flood-prone areas in several test sites in the United States, Africa and Europe. As part of the activities of SaferPLACES, in this study we test different geomorphic classifiers (GFI included) for the identification of flood-prone areas in a wide area in Northern Italy (c.a. 100000 km2, including Po, Adige, Brenta-Bacchiglione and Reno river basins). We refer to the recently compiled MERIT (Multi-Error-Removed Improved-Terrain) DEM, a 3sec-resolution (~90m at the equator) DEM developed by removing multiple error components from existing spaceborne DEMs. As reference maps for the calibration, we select the flood hazard maps provided by (i) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Center (JRC) of the European Commission. Our study confirms the better performances of GFI compared to other geomorphic classifiers, also providing useful information regarding the sensitivity of GFI threshold values relative to different reference hazard maps; it also suggests as a promising avenue for future researches the combination of multiple geomorphic indices through data-driven approaches and artificial intelligence.

How to cite: Castellarin, A., Persiano, S., Samela, C., Magnini, A., Bagli, S., Mazzoli, P., and Luzzi, V.: Fluvial flooding hazard assessment in Northern Italy: potential and informativeness of different geomorphic classifiers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19760, https://doi.org/10.5194/egusphere-egu2020-19760, 2020.

D1878 |
| Highlight
Sudershan Gangrade, Mario Morales-Hernandez, Ahmad A. Tavakoly, Kristi R. Arsenault, Jerry Wegiel, Kimberly McCormack, Mark Wahl, Sujay V. Kumar, Christa D. Peters-Lidard, Shih-Chieh Kao, and Katherine J. Evans

This work provides an envisioned overview of scientific collaboration among multiple United States agencies including the National Aeronautics and Space Administration (NASA), U.S. Army Engineer Research and Development Center (ERDC), Oak Ridge National Laboratory (ORNL), and National Geospatial-Intelligence Agency (NGA) for the integration of existing data and model capabilities to support global scale water security applications. The primary objective is to develop a high-resolution, operational streamflow and flood forecasting system at the global scale, leveraging multiple process-based models, remote sensing data assimilation, and high-performance computing techniques. We present a preliminary case study that demonstrates the integration of the modeling framework using NASA’s Land Information System (LIS), ERDC’s Streamflow Prediction Tool (SPT), and ORNL’s GPU-accelerated 2D flood model (TRITON). Using the high-resolution terrain data from NGA, a historic flood event that occurred in March 2019 at Offutt Air Force Base in Nebraska, USA, was simulated on ORNL’s supercomputer, Summit. This benchmark test case is used to validate the modeling framework and to help establish a roadmap for the expanded modeling efforts at the global scale. In a broader sense, the proposed infrastructure will enable decision-makers to address issues such as transboundary water conflicts, flood and drought monitoring, and sustainable water resources management and to study their impacts on human, water-energy and natural systems in the short, medium and long term.

How to cite: Gangrade, S., Morales-Hernandez, M., Tavakoly, A. A., Arsenault, K. R., Wegiel, J., McCormack, K., Wahl, M., Kumar, S. V., Peters-Lidard, C. D., Kao, S.-C., and Evans, K. J.: Towards the Development of a High-resolution, Global Streamflow and Flood Forecasting System – An U.S. Interagency Collaboration Effort, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10285, https://doi.org/10.5194/egusphere-egu2020-10285, 2020.

D1879 |
Huili Chen, Qiuhua Liang, Jiaheng Zhao, and Xilin Xia

Glacial lake outburst floods (GLOFs) are one of the major natural hazards in certain populated mountainous areas, e.g. the Himalayan region, which may lead to catastrophic consequences including fatalities. Evaluating the potential socio-economic impact of GLOFs is essential for mitigating the risk of GLOFs and enhancing community resilience. Yet in most of the cases, the impact evaluation of potential GLOFs is confronted with limited data availability and inaccessibility to most of the glacial lakes in the high-altitude areas. This study aims to exploit recent advances in Earth Observation (EO), open-source data from different sources, and high-performance hydrodynamic modelling to innovate an approach for GLOF risk and impact assessment. GLOF scenarios of different glacier dam breach width and depth are designed according to high-resolution aerial imagery and terrain data acquired from unmanned aerial vehicle surveying. High-performance hydrodynamic model supported by open-source multi-resolution data from the latest EO technologies is used to simulate the flood hydrodynamics to provide spatial and temporal flood characteristics. Detailed information on communities and infrastructure systems is collected and processed from multiple sources including OpenStreetMap, Google Earth, and global data products to support impact analysis. The evaluation framework is applied to Tsho Rolpa glacial lake in Nepal, which has been identified as one of the potentially dangerous glacial lakes that may create GLOFs to threaten the downstream communities and infrastructure. According to the simulation results, the worst GLOF scenario can potentially inundate 27 villages, 583 buildings and 20.8 km2 of agricultural areas, and pose high risk to 1 airport, 1 hydro power plant, 3 bus stations, and 21 bridges. Additionally, the spatial and temporal flood simulation results, including water depth, flow velocity and flood arrival time may help identify impacted sites and objects, which would be valuable for the development of evacuation plans and early warning systems.

How to cite: Chen, H., Liang, Q., Zhao, J., and Xia, X.: High-resolution glacial lake outburst flood impact evaluation using high-performance hydrodynamic modelling and open-source data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3714, https://doi.org/10.5194/egusphere-egu2020-3714, 2020.

D1880 |
Alvaro Prida, Manuel Andres Diaz Loaiza, Jeremy Bricker, Oswaldo Morales, Remy Meynadier, Trang Duong, Rosh Ranasinghe, and Arjen Luijendijk

Bayesian Networks for storm surge estimation in Mississippi (US)

A. Prida1, A. Diaz Loaiza1, J. Bricker1, R. Meynadier2, O. Morales-Napoles1, T. Duong3, R. Ranasinghe3, A. Luijendijk1

The unprecedented damage due to flood caused by hurricanes like Katrina (2005) has reinforced the interest of the hydraulic community to improve the storm surge estimation for the North Gulf of Mexico. Very high-resolution hydrodynamic models have been traditionally used for this end. However, these models are computationally very expensive. In this paper, a Bayesian Network (BN) is built to estimate storm surge at the coastal areas of Mississippi. A catalogue of HURDAT2 historical hurricanes is simulated in Delft3D FM to generate a surge data base that is used for the training of the Bayesian Network. The storm surge obtained from Delft3D FM is validated against observations recorded during a past historical event. The landfall location, the maximum wind speed, the forward speed and the forward direction of the hurricane at landfall are the other variables considered in the Bayesian Network. The Bayesian Network is validated by inferring values from past historical events in the model and comparing the modeled surge to observations.

How to cite: Prida, A., Diaz Loaiza, M. A., Bricker, J., Morales, O., Meynadier, R., Duong, T., Ranasinghe, R., and Luijendijk, A.: Bayesian Networks for storm surge estimation in Mississippi (US), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21878, https://doi.org/10.5194/egusphere-egu2020-21878, 2020.

D1881 |
| Highlight
Thaine H. Assumpção, Ioana Popescu, Andreja Jonoski, and Dimitri P. Solomatine

The calibration and validation of inundation models have since long been influenced by data availability. When only stage hydrographs and high water level marks were available, metrics such as the Root Mean Square Error (RMSE) were selected for goodness-of-fit assessment. When remotely sensed flood extent data started to be obtained, binary performance measures started being used. Although data availability and modelling resolution have advanced in the past decades, the methods behind performance evaluation remain similar. Shape-based metrics used in topology and pattern recognition could enhance not only the raw model performance but our ability to diagnose achieved results. Therefore, in this study, we discuss how much improvement in calibration can be obtained by employing shape matching metrics. The research is conducted in two experiments: a 2D hydrodynamic benchmarking model and the Po River case study. Different metrics traditionally used in inundation modelling and metrics tailored towards shape matching were employed. Calibration of the Manning coefficient was performed using one metric at a time. Experiments showed that metrics incorporating scale components (e.g. differences in areas and/or distances) provide better calibration. This corroborates the wide use of traditional metrics and indicates the potential of using shape-based metrics, which can augment our ability to diagnose models and improve modelling results.

How to cite: Assumpção, T. H., Popescu, I., Jonoski, A., and Solomatine, D. P.: Can performance metrics accounting for the flood extent shape improve inundation model calibration?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18039, https://doi.org/10.5194/egusphere-egu2020-18039, 2020.

D1882 |
Frederik Kratzert, Daniel Klotz, Guy Shalev, Sella Nevo, Günter Klambauer, Grey Nearing, and Sepp Hochreiter

Floods are among the most destructive natural hazards in the world. To reduce flood induced damages and casualties, streamflow forecasts should be as accurate as possible.

As of today, streamflow forecasts are usually made with either conceptual or process-based hydrological models. The problem these models usually have is that they perform best when calibrated for a specific basin, and performance degrades drastically if the models are used in places without historic streamflow measurements. To make things worse, some of the most devastating floods occur in developing and low-income countries, where historic records of streamflow measurements are scarce. Therefore, a central task for enhancing flood forecasts and helping local authorities to manage these areas is to provide high-quality streamflow forecasts in ungauged rivers. Although the IAHS dedicated an entire decade (2003-2012) to advance the problem of Prediction in Ungauged Basins the central goal remains largely a challenge.

In this talk, we will present a novel approach for tackling the problem of prediction in ungauged basins using a data-driven approach. More concretely, we show that the Long Short-Term Memory network (LSTM), which is a special type of a deep learning model, can serve as a generalizable rainfall-runoff simulation model. We will present recent results indicating that the LSTM gives on average better out-of-sample predictions (ungauged prediction) than e.g. the SAC-SMA in-sample (gauged) or the US National Water Model (Kratzert et al., 2019).

One place where these research results are already finding their way into operation is Google’s Flood Forecasting Initiative. The goal of this initiative is to provide (enhanced) flood warnings, where needed, starting with a pilot project in India. And as mentioned above, historic streamflow records in those regions are scarce, which motivates new and innovative approaches for enhanced streamflow forecasting.


Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55. https://doi.org/10.1029/2019WR026065, 2019.

How to cite: Kratzert, F., Klotz, D., Shalev, G., Nevo, S., Klambauer, G., Nearing, G., and Hochreiter, S.: Towards deep learning based flood forecasting for ungauged basins, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8932, https://doi.org/10.5194/egusphere-egu2020-8932, 2020.

D1883 |
Hund-Der Yeh, Kuo-Chen Ma, Tze-Y Chan, and Mo-Hsiung Chuang

Floods and droughts are exacerbated due to global warming and climate change. Heavy rainfall often leads to serious flooding events. How to improve traditional methods for storm sewer system design or alternative measures therefore has become an important issue in Taiwan. The objective of this study is to use the SWMM module to simulate the use of the JW eco-technology (JWET) in an area under different heavy rainfall resulting in surface runoff and infiltration. A small region in a city in north Taiwan is selected as the target area for the simulations and the results are compared with the flood potential map produced based on the simulation results from the SOBEK model developed by Deltares System for river, urban or rural management. The low-impact development module of the SWMM is chosen to simulate the spatial distributions of surface runoff and infiltration using the JWET in the target area under different heavy rainfall intensities. The results show that the implement of JWET to the target area can effectively reduce surface runoff and significantly increase surface infiltration and groundwater recharge. In other words, the implement of JWET to an urban area can achieve the objective of environmental adaptation and reduce the loss of people's lives and property.


Keywords: heavy rainfall; low impact development; JW ecological technology

How to cite: Yeh, H.-D., Ma, K.-C., Chan, T.-Y., and Chuang, M.-H.: Implement of JW ecological technology to an area under heavy rainfall, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2086, https://doi.org/10.5194/egusphere-egu2020-2086, 2020.

D1884 |
Shangzhi Chen, Feifei Zheng, and Qingzhou Zhang

With the possible climate change and increased pace of urbanization in the century, urban flooding has caused more and more attentions nowadays. Shallow water equations are widely used to reproduce the flow hydrodynamics of flooding around the urban areas, which have been proved a powerful tool for flood risk assessment and evacuation management, like river flow or flowing at drainage networks with irregular cross-sections at 1D scale. Over the last two decades, Godunov-type schemes have became popular for its robustness treating complex flow phenomenons. When tacking complex topography in the framework of Godunov-type scheme, sourer term needs to be treated property to preserve steady state, that flux gradient and sourer term are balanced. Capart et al. (2003) reconstructed the momentum flux by considering the balance of hydrostatic pressure with the approximated water surface level, which has the ability to tackle the irregular and non-prismatic channel flow with complex topography. This approximation is exact for two cases: 1) rectangular and prismatic channel; 2) water surface is horizontal. However, for other cases, approximation is employed to achieve the hydrostatic equilibrium, which has reduced the accuracy of the numerical solution and increased the complexity for the model implementation. 

In this work, we present a new well-balanced numerical scheme for simulating 1D frictional shallow water flow with irregular cross-sections over complex topography involving wetting and drying. The proposed scheme solves, in a finite volume Godunov-type framework, a set of pre-balanced shallow water equations derived by considering pressure balancing (Liang and Marche, 2009). HLL approximated Riemann solver is adopted for the flux calculation at the cell interface. Non-negative reconstruction of Riemann state (Audusse et al., 2004) and local bed modification (Liang, 2010) produce stable and well-balanced solutions to shallow water flow hydrodynamics. Bed slope source term can be approximated using central difference and no special treatment is needed for wet and dry bed. The friction source term is discretized using a splitting implicit scheme and limiting value of friction force is used to ensure stability for the dry bottom (Liang and Marche, 2009). The new numerical scheme is validated against two theoretical benchmark tests and then compared with the validated shallow water model with circular and trapezoid cross-sections over complex topography involving wetting and drying. This method is also possible to reproduce the mixed flow in the conduit or for the flow with non-prismatic channel like river flow in the near future.


Audusse, E., Bouchut, F., Bristeau, M. O., Klein, R., & Perthame, B. T. (2004). A fast and stable well-balanced scheme with hydrostatic reconstruction for shallow water flows. SIAM Journal on Scientific Computing, 25(6), 2050-2065.

Capart, H, Eldho, TI, Huang, SY, Young, DL, and Zech, Yves, "Treatment of natural geometry in finite volume river flow computations", Journal of Hydraulic Engineering 129, 5 (2003), pp. 385--393.

Liang, Qiuhua and Marche, Fabien, "Numerical resolution of well-balanced shallow water equations with complex source terms", Advances in water resources 32, 6 (2009), pp. 873--884.

Liang, Qiuhua, "Flood simulation using a well-balanced shallow flow model", Journal of hydraulic engineering 136, 9 (2010), pp. 669--675.

How to cite: Chen, S., Zheng, F., and Zhang, Q.: 1D flow simulation with irregular cross-sections using the pre-balanced shallow water equations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3828, https://doi.org/10.5194/egusphere-egu2020-3828, 2020.

D1885 |
Byunghyun Kim, Hyun Il Kim, and Kun Yeun Han

Unexpected disastrous floods or flash floods caused by climate change are becoming more frequent. Therefore, there is a possibility of dam failure due to natural disasters including heavy rainfall, landslide and earthquakes, and an unexpected emergencies may be caused by the defect of dams or appurtenant structures due to the aging of the dam. It is desirable to prevent in advance because emergencies such as dam failure can cause many casualties and property damage.

Dam failure rapidly propagates enormous flow to the downstream, so the evacuation time is short and causes many casualties compared to other types of floods. In order to minimize casualties from dam failure, it is important to establish emergency action plan, flood hazard map and advance warning system. For the establishment of these three, accurate dam failure modeling is required. Most of the studies on dam failure modeling have been conducted for single dam failure rather than successive failure of two or more dams. This study conducted a successive failure modeling of Janghyun Dam and Dongmak Dam in Korea, which collapsed due to Typhoon Rusa in 2002.

The DAMBRK (Dam-Break Flood Forecasting Model) has been applied to the successive failure modeling of two dams which are located in parallel. The relaxation scheme was added to DAMBRK to consider the tributary cross-section. In addition, this study proposed a method to estimate the dam failure duration using empirical formulas for the peak discharge of dam failure and failure formation time of ASDSO (Association of State Dam Safety Officials). The failure hydrograph of two dams was estimated using the proposed method and the discharge and water surface elevation were predicted at the main locations of downstream according to the propagation of dam failure discharge. The accuracy and applicability of the modeling were validated by comparing the predicted water surface elevations with field surveyed data and showing good agreements between predictions and measurements.

Keywords:  Successive Dam-Break, Flooding, DAMBRK


This work was supported by Korea Environment Industry & Technology Institute(KEITI) though Water Management Research Program, funded by Korea Ministry of Environment(MOE)(79609)

How to cite: Kim, B., Kim, H. I., and Han, K. Y.: 1-D Dam-Break Modeling: Case Study of Successive Dam-Break, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21120, https://doi.org/10.5194/egusphere-egu2020-21120, 2020.

D1886 |
Dhruvesh Patel, Raviraj Dave, Amit Kumar Dubey, Praveen Kumar Gupta, and Raghavendra Singh

Catastrophic flood leads to a major disaster in developing countries. It loses a life and significant valuable properties, therefore it assessment is a prime requirement to identify the risk and vulnerable area in a flood-prone region. Many hydrodynamic models are providing a solution to identify the flood inundation area, flood arrival time, and velocity of flow in flood susceptible area, however, due to the low resolution of DEM, it can’t assess the actual flooding condition. To overcome this limitation, the present study describes the creation of high resolution (3 cm gridded) DEM for Dhanera city, Rel river catchment in Gujarat where it was affected by the catastrophic flood in the year of 2015 and 2017. Phantom 4 Pro RTK, DGPS and Pix4 software are used for creation of high-resolution DEM. The entire 10 km2 area of Dhanera city is divided 4 blocks and each block is mapped by Phantom 4 pro-RTK Unmanned Aerial Vehicle (UAV) at 80 % image overlaps. A total of 9222 images are captured and post-processed using a Pix 4 software. Ground Control Points were marked for rectification in the geo-location of aerial images using DGPS (RTK). The aerial images collected during the survey have a spatial resolution of 3 cm with geo-location. The data collected is put for post-processing using Pix4D mapper software. 3D classified point cloud, DTM and DSM of 3 cm spatial resolution, orthomosaic of 3 cm spatial resolution are produced after the processing. Generated High-resolution DEM (DTM & DSM) will be utilized for hydrodynamic modeling to produce a precise flood inundation maps. 


Acknowledgement: The corresponding author is thankful to the ORSP, PDPU, and SAC-ISRO, SARITA program for providing the research grant to execute the work. (Grant no: ORSP/R&D/SRP/2019/MPDP/007; SAC/EPSA/GHCAG/LHD/SARITA/01/19)

How to cite: Patel, D., Dave, R., Dubey, A. K., Gupta, P. K., and Singh, R.: High-resolution DEM Creation using a UAV for Flood Inundation Hydrodynamic Modeling- A Case of Rel River Flood, Gujarat, India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5448, https://doi.org/10.5194/egusphere-egu2020-5448, 2020.

D1887 |
Yiheng Chen, Lu Zhuo, and Dawei Han

Cities are the place where a large portion of the population lives. Traditional urban planning models usually based on separate functions of a city or region. A coherent city model is a newly developed tool to take the interaction between each section into consideration. The city model in this paper focuses on the water system infrastructure because flood risk is becoming an increasingly challenging issue with the rapid urbanization and extreme weather under climate change. The paper aims to give a timely review of the development of city models from various originates. Then, it introduces a number of popular modelling techniques that have been demonstrated useful or may be of potential usage for city modelling purpose, such as GIS, CIM, ABM, etc. The review of model techniques provides the readers with suggestions on how to choose the technique to deal with their own research question. After that, this paper also points out the possible future directions of city models with challenges requiring further research efforts.

How to cite: Chen, Y., Zhuo, L., and Han, D.: Review of city models and the applications on flood risk management, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11947, https://doi.org/10.5194/egusphere-egu2020-11947, 2020.

D1888 |
Chien-Nien Chen, Miguel Rico-Ramirez, Dawei Han, and Ahmed Abdelhalim

This research is part of the ongoing research project Climate Change Adaptation to ManagE the Risks of Extreme HydrologicaL and Weather Events for Food Security in Vulnerable West Nile Delta (CAMEL). The study area−West Nile Delta−is an important region in Egypt in terms of agricultural and industrial productions, whilst it is a vulnerable area facing extreme weather and environmental crises (e.g. flooding, soil salinization, and sea level rise), as well as in socio-economic respect. In the latest decades, the region suffered more weather extremes due to climate change; the severe rainfall events resulted in flooding causing heavy casualties and economic loss. Therefore, the project aims to build an integrated flood early warning system for Egypt. However, in order to tackle the issue of data scarcity of ground observation, this research seeks to apply satellite precipitation observation and numerical weather prediction (NWP) as the substitution (i.e. GPM, MPE and ECMWF data) and to develop an approach with the integration of Nowcasting and NWP for precipitation forecasting.

Generally known that Nowcasting method and NWP both have limitations in performing local convective and formative precipitations, whilst in different reasons. The research seeks to improve this effect in Nowcasting as it has advantage in short term performance (i.e. a few hours) whilst NWP has advantage in long term performance (i.e. a few days). The findings from the vector field of Nowcasting indicate that the relativity between shift speed and shape changing speed of precipitation is the key for accurate prediction, which is the disadvantage of the optical flow approach of the Lagrangian method that Nowcasting applies as the main stream core. The research hence applies a machine learning approach−support vector machine (SVM)−to figure out the relativity aforementioned to identify disadvantage data that needs to be pre-treated prior to the Lagrangian Nowcasting. Meanwhile, by applying a phase-based frame interpolation method based on the Eulerian method to downscale the temporal resolution, it can improve these disadvantage data identified by machine learning so as to better perform in the Lagrangian Nowcasting. The integrated Nowcasting approach is expected to have better performance in forecasting and still retains low computational resource consumption.

How to cite: Chen, C.-N., Rico-Ramirez, M., Han, D., and Abdelhalim, A.: An Integrated Nowcasting Approach with Machine Learning for Applying Global Sensing Datasets to Forecast Precipitation Extremes in Data-scarce Nile Delta, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11380, https://doi.org/10.5194/egusphere-egu2020-11380, 2020.

D1889 |
Liang Qiuhua, Yan Xiong, and Gang Wang

Under climate change, extreme weather events such as storms and intense rainfall has become far more frequent. This is evidenced by the outburst of multiple flood events in the recent years in the UK and other parts of the world. Induced by intense rainfall, flash flooding is one type of wide-spread natural hazards that can pose serious threats to people’s lives and properties. Most likely happening in steep rapid-response catchments following localized high intensity rainfall, flash floods are characterized by rapid rise of water level and high flow velocities in channels and floodplains. The violent flood waves can remove and transport heavy objects such as cars and tree, imposing extra risk to people and infrastructure, e.g. bridges.

On 16th August 2004, the coastal village of Boscastle in north Cornwall, UK, was devastated by a flash flood following an exceptional amount of rain that fell over eight hours. The village suffered extensive damage and notably, some 100 vehicles were washed to downstream and into the sea, some of which blocked bridges and altered flood hydraulics. This work aims to reproduce the flood event including floating debris dynamics using a new coupled hydrodynamic model. The coupled modelling tool predicts the flooding process using a finite volume shock-capturing model that solves the fully 2D shallow water equations (SWEs), which is coupled with a discrete element model (DEM) to simulate the interactive dynamics of floating objects. The coupled model is further accelerated by implementation on modern GPUs and is therefore well-suited for simulation of large-scale transient flood hydrodynamics enriched with floating debris. The simulation results are first confirmed by comparing with maximum flood depths collected after the event. Further simulations are carried out to investigate the influence of floating vehicles on flood hydrodynamics and understand how they block bridges and alter flood paths. The simulation results are consistent with observations captured during the event.

Key Words: Flash flooding; Hydrodynamic model; Shallow water equations; Discrete element model; Floating debris

How to cite: Qiuhua, L., Xiong, Y., and Wang, G.: Simulation of Floating Debris during a Flash Flood Event, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4251, https://doi.org/10.5194/egusphere-egu2020-4251, 2020.

D1890 |
Anastasiia Mironenko, Ekaterina Rets, and Natalia Frolova

The result of maximum water levels variability analysis along with the information of the frequency of adverse and dangerous hydrological phenomena exceeding levels and fluctuations maximum amplitude of water levels are presented in this research. There are two periods of comparison of the water levels recorded at 146 hydrological gauges – 1926-1975 and 1976-2015. Statistical analysis of databases was selected as the main research method including agreement criteria with parametric and nonparametric criteria of homogeneity.

The recent rise in mathematical expectation of maximum water levels is a characteristic for all the North Caucasian rivers. Maximum water levels dispersion have a tendency to decrease in the south of the Black Sea Caucasian coast, the Psheha and the Belaya rivers, the Sulak and the Fortanga rivers, the Baksan upstream. The remaining gauges recorded an increase in water levels dispersion, which is the predominant trend for the North Caucasian rivers.

The frequency of the adverse events exceeding water levels reaches 50% on the Afips, the Belaya, the Kuma, the Laba, the Mzymta, the Ubinka and the Vulan rivers. By the number of hazard levels exceeded, the areas adjacent to the Kuma, the Laba, the Psekups, the Pshish and the Ubinka are most susceptible to the floods.

Another part of the framework was connected with potential flood-affected region mapping over the North Caucacus. Thus, a map of potential flood zones caused by North Caucasian rivers was created according to maximum water levels recorded at 232 hydrological gauges.

This study was funded by RFBR according to the research project № 20-35-70024.

How to cite: Mironenko, A., Rets, E., and Frolova, N.: Variability assessment of flood hazard indicators on the North Caucasus, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11512, https://doi.org/10.5194/egusphere-egu2020-11512, 2020.

D1891 |
Sonja Teschemacher and Markus Disse

A considerable share of the losses by extreme flooding are in upstream areas, where centralized flood mitigation measures have no effect. Consequently, modern flood mitigation strategies address this problem by a distributed combination of measures, including nature-based solutions and decentralized flood detention basins. These small basins can be realized by minor changes in the landscape and can influence the runoff behavior at the site and downstream. However, the economic viability of the sites and the local and regional effectiveness depend on the location optimization, which is influenced by the local topography as well as by complex superposition effects.
We address this complexity with a combination of two innovative and automated optimization tools: LOCASIN (LOCation detection of retention and detention bASINs) is a flexible tool to automatically detect, characterize and evaluate detention basin locations. It is based on topographical data and provides information on the basin geometry as well as on the required curves for basin retention calculations. TOBAS (Tool for the Optimization of BASin efficiencies) calculates the effectiveness of a basin combination, taking into account the mutual influence when optimizing the throttle size. The input data includes the relation of water level and retention volume from LOCASIN and hydrographs generated by a hydrological model (e.g. WaSiM). Furthermore, TOBAS can be applied to select and dimension an optimized basin combination from the locations determined with LOCASIN. The optimization is based on the respective objective, e.g. effectiveness or economic efficiency. Hence, the joint application of both tools can contribute to improve efficient flood mitigation strategies and enhance flood resilience. The applicability of the tools and the benefits for the assessment of flood mitigation concepts were tested and confirmed by means of different Bavarian catchments.

How to cite: Teschemacher, S. and Disse, M.: Automated location optimization of detention basins as a contribution to an efficient flood mitigation strategy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18342, https://doi.org/10.5194/egusphere-egu2020-18342, 2020.

D1892 |
Tim Winkels, Willem Jan Dirkx, Kim Cohen, Hans Middelkoop, Rens van Beek, Marc Bierkens, and Esther Stouthamer

River embankments form an essential part of the primary flood defence in the Netherlands. Of all failure mechanisms, piping is considered one of the key mechanism for triggering dike destabilization of river dikes in Rhine-Meuse Delta. Within the STW project Piping in practice, we aim to better understand 1) the influence of variability within subsurface characteristics on the piping process, and 2) the natural variability of these subsurface characteristics underneath embankments in the Rhine-Meuse delta.  
We employ the lithogenesis of sandy deposits to group variability in subsurface parameters across different scales. Using extensive borehole datasets, we quantified regional trends within and between geological units in order to investigate geological controls on variability these subsurface properties. On a smaller scale, laboratory experiments have shown that larger variation in grain size or layering in porous media have a retarding effect on the progression of small-scale pipes, demonstrating the importance of incorporating these variabilities into the piping assessments.  Combining laboratory experiments and field observations, representative sedimentary architectures are implemented into digital piping models at several embedded scales. This will allow us to better describe subsurface variability in terms of model parameters, and improve computation of the piping process.

How to cite: Winkels, T., Dirkx, W. J., Cohen, K., Middelkoop, H., van Beek, R., Bierkens, M., and Stouthamer, E.: Piping in practice: Incorporating natural subsurface variability into backward erosion models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21349, https://doi.org/10.5194/egusphere-egu2020-21349, 2020.

D1893 |
B. Thanga Gurusamy, Avinash D Vasudeo, and Aniruddha Dattatraya Ghare

Abstract: Because of the uncertainty and high cost involved, the Absolute Flood Protection has not been considered as a rational decision. Hence the trend is to replace Absolute Flood Protection strategy by Flood Risk Management Strategy. This Paper focus on the development of Multiple Criteria Decision Making (MCDM) model towards Flood Risk Management (FRM) across Godavari Lower Sub-Basin of India using GIS based methodologies for Flood Hazard Zonation in order to achieve global minimum of the Flood predicted Risk level.  Flood Hazard Zone Map for the historical flood events obtained with the use of GIS based Digital Elevation Models across the study area have been presented and used for the estimation of Hazard Risk. Uncertainty (or Control) Risk levels of each Flood estimated using various Flood Forecasting methodologies have been compared for the selected locations of the study area. Effectiveness of Passive Flood Protection Measures in the form of Flood Levees has been quantitatively analyzed for the increase in the Opportunity Risk and corresponding reduction in the Flood Hazard Risk. Various types of Multi-Objective Evolutionary Algorithms (MOEAs) have been used  to determine a Compromise solution with conflicting criteria between Hazard Risk and Opportunity (or Investment) Risk and the results were compared for each of the selected levels of Flood estimated with corresponding uncertainty. Traditional optimization method in the form of Pareto-Optimal Front have also been graphically depicted for the minimization of both Hazard Risk Objective function and Opportunity Risk Objective Function and compared with those obtained using MOEAs. Watershed wise distribution of optimized Flood Risk variation across the Sub-basin has been presented graphically for both the cases of with and without active Flood Routing Measures. Keywords:  Flood Risk Management; GIS based Flood Hazard Zonation; Multi-Criteria Decision Making; Multi-Objective Evolutionary Algorithms; Godavari Lower Sub-Basin of India;

How to cite: Gurusamy, B. T., Vasudeo, A. D., and Ghare, A. D.: GIS based Development of MCDM Model for Flood Risk Management across Godavari Lower Sub-Basin of India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4605, https://doi.org/10.5194/egusphere-egu2020-4605, 2020.

D1894 |
Sanjay Yadav, Surendra Borana, Namrata Jariwala, and Shri Ram Chaurasia

The present study evaluates floods of semi-arid region with flat topography. The flood water spread over hundreds of square kilometer being delta region of Sukhbhadar River. The government of Gujarat aims to develop study region which spreads over 920 sq. km as Dholera Special Investment Region (DSIR). The study area is highly prone to flooding due to confluence of number of rivers namely Sukhbhadar, Lilka, Utavli and Padalio and tidal ingress from Gulf of Cambay.

Sukhbhadar River enters the DISR area near village Kashindra and flows through the Town Planning Scheme -TP1 and one of its tributaries Adhiya River, originating from village Cher flows through the Town Planning Scheme - TP2 of DSIR and meets the Sukhbhadar River at village Khun. The Sukhbhadar River is one of the major river passing through TP1 and TP2. The Sukhbhadar Dam is one of the major Dam on the Sukhbhadar River and it is approximately 100 km upstream of the study area. The average annual rainfall on the downstream side of the Dam and of DSIR region is 701 mm. In the year 2019 August there was heavy rainfall. The releases from the Sukhbhadar Reservoir and rainfall resulted into catastrophic floods in these regions of DSIR. As DSIR is special region proposed to develop for industrial activities, floods may cause millions of dollars damages in future. In the present study 1- Dimensional Hydrodynamic modelling has been carried out for recent flood of year 2019. MIKE 11 software is used to model 1D unsteady flow for this event. The shape file of the Sukhbhadar River reach from Sukhbhadar Dam to DSIR region is given as input and cross sections at regular interval of 100 m are generated from AW3D30 DEM. Sukhbhadar Dam release hydrograph is given as upstream boundary condition and predicted Tidal data of Bhavnagar is given as downstream boundary condition. It has been observed that from Sukhbhadar Dam to 55481.3 chainage slope is 1 in 698. For 55481.3 to 1611.33 chainage the slope is 1 in 3591. The area of DSIR is almost flat. As observed during recent flood of year 2019, entire DSIR area (920 sq km) was fully inundated. It has been felt that strong mitigation measures are required to cope up with these flooding situations. In the present analysis embankment or retaining wall on either bank of the river has been considered as one of the flood mitigation measure. The height of retaining wall to prevent these DSIR areas vary from 1 m to 25 meters up to 2500 cumec releases from the dam. This solution may not be economical hence it is proposed to take advantage of parallel natural streams and ponds to conserve flood water. This solution seems to be more practical and economical. The paper analyze flood of DSIR region as without proper flood measures it is difficult to develop this region.

How to cite: Yadav, S., Borana, S., Jariwala, N., and Chaurasia, S. R.: Assessment of flood effect on semi-arid special investment region using 1D hydrodynamic modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2524, https://doi.org/10.5194/egusphere-egu2020-2524, 2020.