HS6.3 | Remote Sensing for Flood Dynamics Monitoring and Flood Mapping
EDI
Remote Sensing for Flood Dynamics Monitoring and Flood Mapping
Co-organized by NH6
Convener: Guy J.-P. Schumann | Co-conveners: Alessio Domeneghetti, Antara Dasgupta, Nick Everard, Angelica Tarpanelli
Orals
| Thu, 27 Apr, 08:30–10:15 (CEST)
 
Room 3.16/17
Posters on site
| Attendance Thu, 27 Apr, 10:45–12:30 (CEST)
 
Hall A
Posters virtual
| Attendance Thu, 27 Apr, 10:45–12:30 (CEST)
 
vHall HS
Orals |
Thu, 08:30
Posters on site |
Thu, 10:45
Posters virtual
Thu, 10:45
The socio-economic impacts associated with floods are increasing. Floods represent the most frequent and most impacting, in terms of the number of people affected, among the weather-related disasters: nearly 0.8 billion people were affected by inundations in the last decade, while the overall economic damage is estimated to be more than $300 billion.
In this context, remote sensing represents a valuable source of data and observations that may alleviate the decline in field surveys and gauging stations, especially in remote areas and developing countries. The implementation of remotely-sensed variables (such as digital elevation model, river width, flood extent, water level, flow velocities, land cover, etc.) in hydraulic modelling promises to considerably improve our process understanding and prediction. During the last decades, an increasing amount of research has been undertaken to better exploit the potential of current and future satellite observations, from both government-funded and commercial missions, as well as many datasets from airborne sensors carried on airplanes and drones. In particular, in recent years, the scientific community has shown how remotely sensed variables have the potential to play a key role in the calibration and validation of hydraulic models, as well as provide a breakthrough in real-time flood monitoring applications. With the proliferation of open data and more Earth observation data than ever before, this progress is expected to increase.
We encourage presentations related to flood monitoring and mapping through remotely sensed data including: - Remote sensing data for flood hazard and risk mapping, including commercial satellite missions as well as airborne sensors (aircraft and drones);
- Remote sensing techniques to monitor flood dynamics;
- The use of remotely sensed data for the calibration, or validation, of hydrological or hydraulic models;
- Data assimilation of remotely sensed data into hydrological and hydraulic models;
- Improvement of river discretization and monitoring based on Earth observations;
- River flow estimation from remote sensing

Orals: Thu, 27 Apr | Room 3.16/17

Chairpersons: Guy J.-P. Schumann, Antara Dasgupta, Angelica Tarpanelli
08:30–08:35
5-minute convener introduction
Rapid Flood Mapping
08:35–08:45
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EGU23-8520
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HS6.3
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solicited
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Highlight
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On-site presentation
A framework for improved near real-time flood mapping 
Tapio Friberg, Ambika Khadka, and Arnaud Dupeyrat

ICEYE has been a leader in the mapping and monitoring of global floods for the insurance sector and governments over the last two years. Current operational flood monitoring is based on the large-scale and systematic availability of synthetic aperture radar (SAR) data from the small satellite constellation deployed and operated by ICEYE. The main advantages of SAR images are that they provide synoptic views over wide areas, day and night and in all-weather conditions. However, SAR can be less suitable for providing flood extent information in dense urban areas and under tree canopy cover. In addition, SAR-based flood depth generation methods struggle to provide accurate depth estimations in steep terrain. There is currently a demand to aid observational flood models with physically-based flood modeling in urban areas.

Most operational real-time flood estimates are based on predictions of discharges at river flow monitoring stations using 1D hydrological models. 2D inundation models are computationally expensive and thus require special tooling for creating rapid flood maps. In this presentation, ICEYE will describe a framework that can be used for improving the robustness and accuracy of near real-time flood predictions.

How to cite: Friberg, T., Khadka, A., and Dupeyrat, A.: A framework for improved near real-time flood mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8520, https://doi.org/10.5194/egusphere-egu23-8520, 2023.

08:45–08:55
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EGU23-7558
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HS6.3
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On-site presentation
Rapid Flood inundation mapping using SAR data with Google Earth Engine cloud platform 
Qin Wang, Lu Zhuo, Chen Li, Miguel Rico-Ramirez, Zitong Wen, and Dawei Han

Flood events are becoming increasingly common with the increase in the frequency of extreme weather driven by climate change. The present state of the technologies for flood risk mapping is typically tested on small geographical regions due to limitation of flood inundation observations, which hinders the implementation of flood risk management activities. Synthetic aperture radar (SAR) measurements represent an indispensable data source for flood disaster planners and managers, given their ability to scan the Earth's surface nearly independently of weather conditions and the time of day. The decision by the European Space Agency (ESA) Copernicus program to open data from its Sentinel-1 SAR satellites to the public marks the first time of global, operational SAR data freely available. Combined with the emergence of cloud computing platforms like the Google Earth Engine (GEE), this development presents a tremendous opportunity to the disaster response community, for whom rapid access to analysis-ready data is needed to inform effective flood disaster response interventions and management plans. Here, we present an algorithm that exploits available Sentinel-1 SAR images in combination with historical Landsat and other auxiliary data sources hosted on the GEE to rapidly map surface inundation during flood events. Our algorithm relies on multi-temporal SAR statistics to identify historical floods. Additionally, historical Landsat-based surface water class probabilities are used to distinguish floods from permanent or seasonally occurring surface water. Using this algorithm, we can get a flood inundation map of the region of interest in less than 10 seconds. We tested the algorithm over Houston, Texas following the Hurricane Harvey in late August 2017 and the results showed an accuracy of 89.9%. The flexibility of our algorithm will allow for the rapid processing of future open-access SAR data, including data from future Sentinel-1 missions.

How to cite: Wang, Q., Zhuo, L., Li, C., Rico-Ramirez, M., Wen, Z., and Han, D.: Rapid Flood inundation mapping using SAR data with Google Earth Engine cloud platform, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7558, https://doi.org/10.5194/egusphere-egu23-7558, 2023.

08:55–09:05
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EGU23-1603
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HS6.3
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ECS
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On-site presentation
A parsimonious approach to estimate flood depths — also in urban areas — for satellite-based flood maps 
Andrea Betterle and Peter Salamon

Floods are among the most disruptive and widespread natural disaster that kill and displace millions of people every year. To cope with the impacts associated with ongoing floods, it is fundamental to acquire a rapid, accurate and comprehensive overview of inundated areas. Imageries sensed by satellites are becoming indispensable for this purpose, especially those acquired in the radar frequencies (SAR), as they can detect floods during the night and with cloudy skies. However, SAR-based remote sensing has serious limitations when it comes to flood mapping in urban and/or vegetated areas (because of low sensitivity or over-detection issues). Furthermore, satellite-based flood delineation does not provide any information on flood depths, which are critical for emergency response planning and for post-event impact evaluation. This contribution introduces a new framework to estimate water depth and to augment flood mapping where satellites cannot sense floodwater. As input, the method simply requires flood delineation (including the areas excluded from mapping because of the aforementioned limitations) and land surface topography. Although the framework is designed to be coupled with the recently release Global Flood Monitoring system of the Copernicus Emergency Management Service, its range of applicability is wide, provided that the basic input needs are met. The approach is especially suited to enhance flood mapping in systematic large-scale applications that require minimum supervision. 

How to cite: Betterle, A. and Salamon, P.: A parsimonious approach to estimate flood depths — also in urban areas — for satellite-based flood maps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1603, https://doi.org/10.5194/egusphere-egu23-1603, 2023.

09:05–09:15
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EGU23-16793
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HS6.3
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On-site presentation
Flood rapid mapping for immediate response: from semi-automatized delineation to AI-derived estimations 
Sébastien Delbour, Christophe Fatras, and Vera Gastal

In the frame of the Copernicus Emergency Management Service - Rapid Mapping, reliable flood maps must be delivered to users within six hours from the availability of remote sensing data. This data can be of different types, either from optical or SAR datasets, which all present different properties (wavelengths, band availability, resolution, etc.). This production is currently performed using semi-automatic methods and processes, to avoid misclassification and provide flood maps as accurate as possible. In order to improve service delivery performances including for covering very large areas, there is a need of an accurate automatically produced first guess, to eventually be modified manually. This is the main reason why the use of AI to learn and detect flooded areas is explored here for both optical and SAR data. The FloodML project used a random forest approach mixed with an in-house learning database to assess flood maps from both optical and SAR datasets. It showed good results, and can cover automatically a 10,000 km² area in a few minutes only. The success of this first approach led to both FloodDAM and FloodDAM-DT projects. These follow-ons now focus on the detection of water height level irregularities in local river gauges, to then produce flood maps if needed, to potentially lead to a modelling of the flood event evolution through data assimilation.

How to cite: Delbour, S., Fatras, C., and Gastal, V.: Flood rapid mapping for immediate response: from semi-automatized delineation to AI-derived estimations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16793, https://doi.org/10.5194/egusphere-egu23-16793, 2023.

09:15–09:25
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EGU23-11794
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HS6.3
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ECS
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On-site presentation
Observing with Sentinel-1 widespread flood crises of 2022 in Pakistan and Nigeria 
Florian Roth, Mark Edwin Tupas, Bernhard Bauer-Marschallinger, and Wolfgang Wagner

Flood events are a major threat to human lives and are often responsible for a substantial destruction of infrastructure. Unfortunately, the obstruction of transport links often prevents the accessibility of certain regions and the impact cannot be estimated, especially during large scale flood events. In such crises, earth observation data provide the most valuable information. Due to their cloud-independent observations, microwave satellites are well-suited for observing the flood extent in these situations. In 2022, millions of people were affected when large flood events hit Pakistan and Nigeria. Both events were covered by images taken by the European Synthetic Aperture Radar (SAR) satellite Sentinel-1, whereby the event in Pakistan was captured more frequently compared to the one in Nigeria.

The Global Flood Monitoring (GFM) component of the Copernicus Emergency Management Service (CEMS) utilises Sentinel-1 to automatically map floods on a global scale. The service relies on three independent flood mapping algorithms combined to an ensemble solution, and one of them was developed by the Technische Universität Wien (TU Wien). The algorithm (Bauer-Marschallinger et al., 2022) performs a pixel-wise Bayesian decision between flood and no-flood situation. For this, a local no-flood backscatter signature is provided based on a time-series-based harmonic model. The flood backscatter signature is defined by a linear model for water surfaces. Thanks to this setup, the algorithm provides its results without the need for any manual intervention and allows fast and lightweight computation.

This contribution analyses results of the TU Wien algorithm for the two large scale events in Pakistan and Nigeria, and will include the presentation of the affected areas, as well as the temporal progression of the flood crises. The performance evaluation of events of such magnitude generally lacks comprehensive ground-truth data and is commonly performed based on other satellite-derived data. Expanding the scope of a previous study of the Pakistan flood (Roth et al., 2022), we compare the results to other datasets being retrieved from multi-temporal data and cover the larger area of the event. The required reference data were received from a local and global flood mapping service, namely Sentinel Asia and the United Nations, respectively. Finally, the varying Sentinel-1 coverage density in respect to flood progression will be discussed to obtain insights into the impact of the satellite overpass frequency on the flood mapping quality.

 

Bauer-Marschallinger, B., Cao, S., Tupas, M. E., Roth, F., Navacchi, C., Melzer, T., ... & Wagner, W.: Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube, Remote Sensing, 14(15), 3673, 2022.

Roth, F., Bauer-Marschallinger, B., Tupas, M. E., Reimer, C., Salamon, P., and Wagner, W.: Sentinel-1 based analysis of the Pakistan Flood in 2022, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1061, 2022.

How to cite: Roth, F., Tupas, M. E., Bauer-Marschallinger, B., and Wagner, W.: Observing with Sentinel-1 widespread flood crises of 2022 in Pakistan and Nigeria, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11794, https://doi.org/10.5194/egusphere-egu23-11794, 2023.

Flood Monitoring and Modelling Approaches
09:25–09:35
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EGU23-2284
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HS6.3
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ECS
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On-site presentation
Random forest models based on Sentinel-2 multispectral indices for flood mapping 
Cinzia Albertini, George P. Petropoulos, Andrea Gioia, Vito Iacobellis, and Salvatore Manfreda

Optical satellite sensors represent a reference for Earth imaging applications, including land monitoring and flood management, directly allowing the visual interpretation of acquired scenes or the exploitation of surfaces’ spectral signatures. An extensive literature exists that proves the ability of multispectral satellite sensors in mapping flooded areas and water bodies (Albertini et al., 2022). Several multispectral indices have been developed for water segmentation in different contexts of varying degrees of landscape complexity. Simultaneously, the advancements in Machine Learning (ML) methods led to a proliferation of supervised and unsupervised algorithms applied to classification problems in the field of flood hazard and risk mapping. In the present study, four random forest (RF) models were used in combination with three spectral indices, namely the Modified Normalized Difference Water Index (MNDWI), the Normalized Difference Moisture Index (NDMI) and the Red and Short Wave Infra-Red (RSWIR) index, to map the extent of the flood event occurred along the Sesia River (Vercelli, Italy) in October 2020. A Sentinel-2 scene was acquired soon after the flooding event and spectral bands at 20m resolution were used in the analyses. The performances of the RF methods implemented with the use of the mentioned spectral indices were evaluated and compared using as reference map the delineation product delivered by the Rapid Mapping service of the Copernicus Emergency Management Service (CEMS). Results revealed some very interesting findings regarding the performances of the examined methods, which can become a well-established operational technique. Last but not least, the validation framework itself underlined the added value of Sentinel-2 and the Copernicus platform as a robust, rapid and cost-effective solution in flood mapping.

Keywords: floods mapping, spectral indices, machine learning, Sentinel-2, Italy

References:

Albertini, C.; Gioia, A.; Iacobellis, V.; Manfreda, S. Detection of Surface Water and Floods with Multispectral Satellites. Remote Sens., 14, 6005, 2022. (doi: https://doi.org/10.3390/rs14236005).

How to cite: Albertini, C., Petropoulos, G. P., Gioia, A., Iacobellis, V., and Manfreda, S.: Random forest models based on Sentinel-2 multispectral indices for flood mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2284, https://doi.org/10.5194/egusphere-egu23-2284, 2023.

09:35–09:45
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EGU23-12183
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HS6.3
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ECS
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On-site presentation
Flooded area monitoring using SAR image-based water body detection technique 
Wanyub Kim, Seulchan Lee, and Minha Choi

Flood is one of main water disasters and causes damage to human life and property. The spatial and temporal disproportion of precipitation due to recent climate change causes flood worldwide every year. As the severity of flood rises, accurate monitoring of flooded areas is being essential for preparation and adaptation. Due to the wide area-occurring characteristics of flood, the use of remote sensing is effective for detecting of flooded areas. In a SAR image, surface of water body appears smooth, so the backscattering coefficient value is generally low. Conversely, surface of non-water body is rough, so the backscattering coefficient value is high. It is possible to divide water body and non-water body by using the characteristics of the backscattering coefficient and specific threshold value. However, the histogram of the backscattering coefficients around rivers where flood occurs most often has a multi-modal distribution, so there is a limit in detecting water bodies using a threshold value only. In this study, a histogram-based multi-threshold method, an AI-based K-means clustering method, and an object segmentation-based Chan-Vese method were used to detect water bodies before and after floods in Sentinel-1 SAR images. The water/non-water body classification image from the Sentinel-2 optical image was used for validation. If SAR images with high spatial and temporal resolution will be available, it is expected that efficient water disaster management will be possible through near real-time detection of flooded areas. 

How to cite: Kim, W., Lee, S., and Choi, M.: Flooded area monitoring using SAR image-based water body detection technique, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12183, https://doi.org/10.5194/egusphere-egu23-12183, 2023.

09:45–09:55
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EGU23-9120
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HS6.3
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On-site presentation
Bank Height Estimates and Flood Models - Challenges, current practices and recent developments 
Laurence Hawker, Jeffrey Neal, and Richard Boothroyd

Estimating river bank heights is crucial for the accuracy of global flood models. Bank heights determine river-floodplain connectivity, and are used to parametrise channel capacity. Poor bank height estimates can lead to incorrect timings and locations of flood overtopping and erroneous channel capacity, resulting in unsatisfactory flood predictions.

In the current implementation of global flood models, bank heights are estimated by extracting elevations from global Digital Elevation Models (DEMs) at river edges. These elevations, even with the latest DEMs, are often noisy and thus need to be heavily filtered and smoothed. Additionally, the surface water masks used to define river edges often do not match the time of acquisition on the DEMs, leading to inconsistencies. These simple methods for estimating bank heights were introduced during the early stages of global flood model development and have not been revisited in depth. With the emergence of new global DEMs (ALOS AW3D, Coperncius, FABDEM), improved surface water masks from multi-temporal, multi-sensor satellite data and novel image processing techniques, we revisit this problem. We present a new method to estimate bank height across scales, comparing estimates derived from global DEMs with high-quality LiDAR. We map the bank height estimates onto a new FABDEM based river network. Using examples from the UK and USA, we demonstrate the impact of bank height estimates on flood inundation.

How to cite: Hawker, L., Neal, J., and Boothroyd, R.: Bank Height Estimates and Flood Models - Challenges, current practices and recent developments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9120, https://doi.org/10.5194/egusphere-egu23-9120, 2023.

09:55–10:05
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EGU23-7672
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HS6.3
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ECS
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solicited
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On-site presentation
Imagery and Terrain Data Fusion with the Flood Inundation Surface Topology (FIST) Model 
Kevin Dobbs and James Phillips

Satellite imagery provides a unique reference for estimating flood inundation extent that can help characterize flood magnitudes and impacts in support of scientific studies and for operational disaster response. All imagery modalities (multispectral/hyperspectral, panchromatic, synthetic aperture radar (SAR)) suffer from factors that confound accurate spatial representation of flood extent, whether using traditional image classification methods or machine learning-based approaches. Clouds, cloud shadows, tree canopy, tall vegetation, and other factors either obscure the water surface or confuse the classifiers. These can yield results that vary widely when compared to actual flood extents, whether referencing observed data like high-water marks or high-quality hydrodynamic models. In addition, opportunities for imagery collection often do not coincide with maximum flood extent due to satellite access windows, cloud cover impacting optical sensors, or a combination of both. That said, the proliferation of existing and planned commercial and civil sensors across all modalities presents increasing opportunities for timely collection.

In recent years, the quality of terrain data at regional, country, continental, and global scales has continued to rapidly improve. The data include WorldDEM, NASADEM, MERIT DEM, EarthDEM, among others, and many regional to country-scale lidar-derived datasets. The availability of this high-quality data allows for new methods that integrate terrain data with remotely sensed imagery data, to yield accurate and timely representations of flood extent in new ways to support both scientific investigations and disaster response.

However, few methods have been developed that integrate satellite and/or aerial imagery data with terrain data to improve imagery-derived flood products. This paper will present new methods, based on the novel Flood Inundation Surface Topology (FIST) Model, for integration of terrain data with the limited data derived from imagery to provide a more accurate representation of maximum flood extents that overcomes many of the aforementioned limitations of using imagery alone. In addition, The FIST model also produces flood depth grids at the resolution of the native terrain data, which represents a major advance in imagery-derived flood products. We present the fundamental directed graph algorithm that is unique to the FIST model; the data architectures that support a range of applications; and case studies for the use of active flood and post-peak flood imagery to generate inundation extents and depth grids for peak-flood conditions.

How to cite: Dobbs, K. and Phillips, J.: Imagery and Terrain Data Fusion with the Flood Inundation Surface Topology (FIST) Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7672, https://doi.org/10.5194/egusphere-egu23-7672, 2023.

10:05–10:15
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EGU23-11021
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HS6.3
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ECS
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Virtual presentation
Inundation and water quality assessment of the Karun river before and after flooding using remote sensing 
Kiana Yahyazadeh Shourabi, Mohammad Hossein Niksokhan, and Soroosh Roozitalab

Natural hydrological phenomena such as floods are among the most crucial hazards, damaging both urban and rural areas. River floods not only result in human and financial losses, but also alter the quality parameters and biological diversity of the river. Karun is one of the largest and wettest rivers in Iran, and its basin experiences numerous floods every year. In this work, satellite data are used to examine how floods affect the Karun River's quality. Specifically, we use NDWI (Normalized Difference Water Index), NDCI (Normalized Difference Chlorophyll Index), and NDTI (Normalized Difference Turbidity Index) data from Sentinel-2 Optical satellite to assess the water quality before and immediately after flooding. Additionally, Sentinel-1 Synthetic-aperture radar (SAR) satellite data are used to observe changes in the river bed and its inundation. This study demonstrates how Sentinel-2 and Sentinel-1 satellites could be effectively used to study variations in water quality and waterbodies at various periods. The results also show how the waterbody and water quality change before and after the flood.

How to cite: Yahyazadeh Shourabi, K., Niksokhan, M. H., and Roozitalab, S.: Inundation and water quality assessment of the Karun river before and after flooding using remote sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11021, https://doi.org/10.5194/egusphere-egu23-11021, 2023.

Posters on site: Thu, 27 Apr, 10:45–12:30 | Hall A

Chairpersons: Nick Everard, Alessio Domeneghetti
A.70
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EGU23-17400
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HS6.3
Innovating global flood alerting with an ensemble of models and remotely sensed observations 
Bandana Kar, Prativa Sharma, Doug Bausch, Jun Wang, Guy Schumann, and Margaret Glasscoe

At the global level, several flood related tools are available for free, ranging from observations to modeling and forecasting, using field data, remotely sensed observations as well as hydrologic and hydrodynamic models (for more details of available tools, see EOTEC DevNet’s tool tracking capacity building resources for flooding at https://eotec-dev.ceos.org/tools/). In this context, the Global Flood Awareness System (GloFAS) managed by Copernicus, for instance, aims to facilitate response to flooding, particularly in countries that cannot forecast these events on their own.

However, having an EWS available to all globally, with consistent accuracy and reliability, for alerting at different severity levels, will not only aid with reduction of flood impacts, but also assist with improving resilience of these counties. 

In this paper, we present the model of models (MoM), which is an ensembled model that forecasts flood severity daily, globally at sub-watershed level. MoM integrates the outputs of GloFAS, GFMS, and HWRF models to forecast severity and uses MODIS and VIIRS outputs for calibration and validation of severity scores.

The flood severity risk score is used to obtain and process high-resolution Earth observation data to assess flood depth and extent at granular level and estimate flood impact on critical infrastructure.

The flood severity score is used to trigger dissemination of alerts using PDC’s DisasterAWARE® platform.

We present a number of real event cases where MoM has been activated to alert and assist with event response activities, including performance validation with high-resolution satellite flood maps.

How to cite: Kar, B., Sharma, P., Bausch, D., Wang, J., Schumann, G., and Glasscoe, M.: Innovating global flood alerting with an ensemble of models and remotely sensed observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17400, https://doi.org/10.5194/egusphere-egu23-17400, 2023.

A.71
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EGU23-439
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HS6.3
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ECS
Expressive fluxes over Amazon floodplain units revealed by high resolution 2D modelling 
Alice Cesar Fassoni-Andrade, Rodrigo Paiva, Sly Wongchuig, Cláudio Barbosa, and Fabien Durand

Water fluxes in the Amazon River floodplain affect hydrodynamic and ecological processes from local to global scales. These fluxes remain poorly understood due to difficult access and limited data in the Amazon basin. In this study, we characterize the hydrodynamics of eight floodplain units of the central Amazon River (40'000 km2) using the 2D hydraulic model HEC-RAS. Remote sensing data, such as floodplain topography estimated by Landsat images, water surface elevation from altimetry, and surface water extent products, were used for model validation. High resolution modeling improved the representation of river and floodplain discharge, water surface elevation (77 cm accuracy) and flood extent (~80% - high water period, ~52% -low water period). The floodplain is organized in units of about 80 km with upstream inflow and downstream outflow. These gross flows are much larger than the net flows with values of up to 20% of the Amazon River discharge and a residence time around 6 days during floods (several months during low water period). Water extent does not a have strong interannual variability during floods as the volume stored in the floodplain, possibly due to topographic constrains. Significant flood extent and volume hysteresis, as well as active flow and storage zones on the floodplain, highlight the complexity of floodplain hydrodynamics. Extreme floods strongly impact the onset and duration of the flood of up to 2 months and, consequently, on the period of high connectivity with the river. These findings are important for understanding carbon and sediment fluxes, and the effects of climate change on water fluxes and riparian communities.

How to cite: Fassoni-Andrade, A. C., Paiva, R., Wongchuig, S., Barbosa, C., and Durand, F.: Expressive fluxes over Amazon floodplain units revealed by high resolution 2D modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-439, https://doi.org/10.5194/egusphere-egu23-439, 2023.

A.72
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EGU23-1358
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HS6.3
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ECS
A sustainable approach to evaluate the impact of urban sprawl on coastal flooding in Oshiwara watershed, Mumbai, India 
Shray Pathak, Audithan Sivaraman, and Geetha Sambandam

Urban sprawl has emerged to be the most important and expensive part of the ecosystem with so many hazardous effects on the natural environment. Flooding has a tremendous impact on the cities, encompassing the water cycle management by collective disciplines of engineering, environmental, social, and economic sciences. This study focuses on analysing urban flooding and incorporating site-specific sub-catchment spatial strategies and management techniques considering human interactions. The Oshiwara watershed in Mumbai, India was delineated which is responsible for urban flooding along with the storm surges in the study region. The flood inundation mapping was obtained for different return periods by implementing hydrologic-hydraulic modeling and further, spatial hazard zones were identified concerning non-heuristic drivers for the 100-year return period. Subsequently, four impact maps namely infrastructural, social, economic, and environmental were identified along with the overall risk. Management interventions involving flood risk mitigation, stormwater harvesting, and water reuse were analyzed to mitigate these impacts. This provides a sustainable approach to spatially mitigate effects at vulnerable zones, instead of adopting a lumped approach for decision-making. Further, it assists the water planners to deploy planning and management interventions at specific risk locations. Thus, this study provides a suitable platform for urban planners to incorporate decisions by focusing on spatial high-risk locations.  

How to cite: Pathak, S., Sivaraman, A., and Sambandam, G.: A sustainable approach to evaluate the impact of urban sprawl on coastal flooding in Oshiwara watershed, Mumbai, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1358, https://doi.org/10.5194/egusphere-egu23-1358, 2023.

A.73
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EGU23-1374
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HS6.3
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Highlight
The FluViSat project: Measuring streamflow from space with very high resolution Planet satellite video 
Nick Everard, Harry Dixon, Sunita Sarkar, Mark Randall, and Guy Schumann

The measurement of streamflow in the world’s rivers is critical to the management of water as a resource and to predicting and managing the impacts of potentially damaging hydrological events such as major floods. The European Space Agency sponsored FluViSat (Fluvial Video from Satellite) project has successfully demonstrated the potential of very high resolution satellite imagery for the determination of water flow speeds, and hence streamflow rates, using established surface velocimetry techniques.

Video imagery kindly provided by Planet Labs PBC from the 21 satellites in their SkySat constellation was pre-processed to stabilise and georectify it, and then analysed using Space Time Imaging Velocimetry (STIV) techniques to provide water speed vectors across the river’s surface. The method was successfully demonstrated on rivers in Australia, the UK and Africa, with field based validation undertaken where possible. Additionally, a series of six videos was obtained and analysed to provide near a sequence of observations of flood flows on the Indus River in Pakistan during the devastating flooding of 2022.

Benefits of the FluViSat innovation include the ability to observe water flow rates almost anywhere on the planet, the potential for multiple daily repeat observations and largely eliminating the need for locally based people, equipment and infrastructure.

This presentation presents results from the research, explains the methods employed to derive and validate flow speeds, and explores opportunities to further enhance the FluViSat methodology.

How to cite: Everard, N., Dixon, H., Sarkar, S., Randall, M., and Schumann, G.: The FluViSat project: Measuring streamflow from space with very high resolution Planet satellite video, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1374, https://doi.org/10.5194/egusphere-egu23-1374, 2023.

A.74
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EGU23-11838
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HS6.3
Surface runoff estimation in urban areas via remotely sensed greenery and composite curve number 
Guy J.-P. Schumann, Paolo Tamagnone, and Ben Suttor

Traditionally, flood risk maps used by city officials and water resource managers for urban planning, by engineers for adequate flood defence infrastructure design, or by insurers and re-insurers for estimating financial risk exposure are the result of modelling flood hazard of rivers and their associated floodplain lands at different return periods. Often, any of these stakeholders would use the 1:100 return period of fluvial hazard to plan accordingly. 

However, with the climate crises signals clearly present during recent flood disasters, and especially with the 2021 Europe floods, water managers, cities and the financial risk sector are now starting to plan differently and are recognizing the need not only for better and more frequently updated flood risk analysis, particularly in urban areas, but also need to consider pluvial and flash floods that can happen in any part of a river basin and oftentimes take place in headwater areas or off the main river floodplains. Flash flooding greatly impacts urban areas where the storm drainage infrastructure is becoming largely insufficient due to the increasing duration and higher frequency of extreme intense rainstorms. Therefore, model simulations of flood hazard that account for these rather unprecedented types of extremely destructive events are required, and those need to be integrating the newest data from all types of sensors. At the same time, we observe that sustainable, nature-based solutions are now sought after because these solutions offer an inviting alternative to ever changing flood risk, particularly under the present and future climate crisis.  

It is stipulated that increasing healthy urban vegetation cover could reduce this risk and is a form of a nature-based solution for urban areas. Here we combine existing methods from the literature and develop a methodology relating  time-series of satellite-based vegetation maps, topography and soil permeability to estimate excess runoff from intense precipitation. The runoff coefficient is mapped through the use of a composite curve number method.. The method of looking at  the partition between rainfall and runoff is highly correlated to change in land use, and thus changes in vegetation cover. Relying on the NDVI index for green vegetation mapping, the methodology is able to capture the differences in the hydrological response even for seasonal or canopy integrity changes. Looking at different vegetation cover scenarios therefore allows the creation of different runoff responses, and therefore a possible reduction in flood risk.In this paper, we present initial results of this flood risk analysis, the goal of which is to produce runoff change maps at city, urban neighbourhood or city post code level using different scenarios in rainfall amounts from design storms coupled with existing or planned urban vegetation cover scenarios.

How to cite: Schumann, G. J.-P., Tamagnone, P., and Suttor, B.: Surface runoff estimation in urban areas via remotely sensed greenery and composite curve number, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11838, https://doi.org/10.5194/egusphere-egu23-11838, 2023.

A.75
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EGU23-5858
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HS6.3
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ECS
Development and validation of flood inundation models for estuaries 
Grigorios Vasilopoulos, Tom Coulthard, Peter Robins, Charlotte Lyddon, Andrew Barkwith, Nguyen Chien, and Matt Lewis

Estuaries, at the interface between catchment and coast, are vulnerable to flooding from the combination of riverine and marine inputs. High river flows generated from intense precipitation can occur synchronously with high tides and storm surges, amplifying flood hazard. In the United Kingdom 20 million people are estimated to live near estuaries, with estuarine flooding regarded as the costliest impact to these areas and second highest hazard to civil emergency. On-going global warming increases sea-levels and modifies hydroclimate variability, thus affecting river fluxes, tidal maxima and the intensify of storm surges. There is therefore a need for improved methods and tools to understand compound flooding events, their impacts and how they may change into the future. In the present paper we developed a validated flood inundation model for the Conwy estuary in North Wales, one of the flashiest catchments in Britain where flooding makes headline news at least once every year. The Caesar-Lisflood 2D hydrodynamic flow model was combined with a range of publicly available datasets to represent channel bathymetry, land elevation, location and heights of flood defences and the hydraulic roughness across the model domain. The model was forced with recorded time-series (15-minute resolution) of tidal oscillations and river discharge data and validated by comparing simulated water levels against observations from existing water level gauges within the estuarine channel. Flood predictions were validated against observed flood extents extracted from SAR imagery using the Google Earth Engine. Calibrated, ortho-corrected (GRD) C-band interferometric Synthetic Aperture Radar (SAR) images captured by the Sentinel-1 constellation of satellites using a dual-band cross-polarization (VH) was used. SAR images were filtered to remove speckle noise and Otsu’s method of thresholding was adopted to automatically extract inundated areas from each available image. Comparison of model-based simulated flood extents against their SAR-derived equivalents was used as a means to validate the flood inundation model.

How to cite: Vasilopoulos, G., Coulthard, T., Robins, P., Lyddon, C., Barkwith, A., Chien, N., and Lewis, M.: Development and validation of flood inundation models for estuaries, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5858, https://doi.org/10.5194/egusphere-egu23-5858, 2023.

A.76
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EGU23-13431
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HS6.3
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ECS
Mapping surface flow pathways in urban areas using UAV-based thermal imaging in combination with flooding experiments 
Jan Bartsch and Tobias Schuetz

In recent years, heavy rainfall events and resulting flash floods have increasingly caused widespread damage to public and private technical infrastructures in Germany. Flood events occurred often at smaller water bodies or as hillslope surface runoff far from the actual watercourses. During extreme events technical measures are often overloaded, so that in addition to local property protection planned emergency runoff pathways can be designated as an essential element of water-sensitive urban development.

The research project ‘Urban Flood Resilience - Smart Tools’ (FloReST), funded by the German Federal Ministry of Education and Research (BMBF), is exploring those measures to increase the resilience of infrastructures after flash floods.

The aim of this study is the development and demonstration of an experimental setup to improve high-resolution digital mapping of existing surface flow pathways in urban areas using UAV-based thermal imaging in combination with flooding experiments. For this purpose, already known critical points, i.e., dysfunctional emergency drainage sections in the urban infrastructure within the City of Trier, Germany were identified.

Within this setting, during relatively warmer or colder days, respectively, we use artificial water releases as a thermal marker of the potentially emerging surface flow pathways. Combining UAV-based visual (RGB) and thermal (infrared) imaging, high-resolution mapping of the potential surface flow paths and their Thalweg is then possible.

Using a hydrological model allows for determining extreme discharges potentially generated in the connected catchment areas. Based on a digital terrain model the locally surveyed water levels and flow paths are then scaled up to potentially occurring water levels during extreme discharges. Depending on the occurrence probability of the extreme discharges a set of high-resolution GIS-datasets of the emergency surface flow pathways around objects at risk of flooding can be generated.

How to cite: Bartsch, J. and Schuetz, T.: Mapping surface flow pathways in urban areas using UAV-based thermal imaging in combination with flooding experiments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13431, https://doi.org/10.5194/egusphere-egu23-13431, 2023.

A.77
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EGU23-17379
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HS6.3
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ECS
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Flood twin experiment for estimating the potential of satellite observations in shallow-water simulations 
Jean-Paul Travert, Cedric Goeury, Vito Bacchi, Fabrice Zaoui, and Sebastien Boyaval

With more than one billion people exposed to floods throughout the world, this natural hazard is the most common and devastating one, resulting in loss of lives and damaging personal properties or sensitive infrastructures. Numerical models have become essential to forecast and to mitigate their consequences, but they remain uncertain mainly due to the lack of high-resolution data and the inherent uncertainties related to the simplified representation of natural phenomena.

The growing availability of satellite observations distributed in time and space is a valuable source of information for improving flood modelling. Additional data like water level or flood extent can be extracted and used to calibrate numerical models.

This study proposes to analyse the potential of remote sensing data as a complement to in-situ observations (from hydrometric stations) in the calibration process of shallow-water flood numerical models. A two-dimensional twin experiment of an extreme flood event overflowing into the floodplains is carried out on a 50 km reach on the Garonne River in France between Tonneins and La Réole. The roughness coefficients are computed as solutions to an inverse problem mixing both in-situ (pointwise and high-frequency) and satellite observations (spatially distributed but low-frequency) data. Data assimilation combining uncertain model simulations and observations has proven efficient for improving hydraulic models. However, an open question is the choice of the best information to assimilate (water level or/and flood extent maps) into the hydraulic models. We study this problem by testing different assimilation configurations. The satellite observations are not considered perfect, so the numerical solutions are compared with different noise levels.

How to cite: Travert, J.-P., Goeury, C., Bacchi, V., Zaoui, F., and Boyaval, S.: Flood twin experiment for estimating the potential of satellite observations in shallow-water simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17379, https://doi.org/10.5194/egusphere-egu23-17379, 2023.

A.78
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EGU23-1061
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HS6.3
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ECS
Multi-scenario flood risk assessment: A case of Sagar Island, West Bengal, India 
Praneta Nadupalli, Aishwarya Narendr, and Bharath H. Aithal

Coastal landscapes are the major source of income and resources. Despite their high vulnerability to
coastal hazards, they are the homes of millions worldwide. Coastal floods are one of the most life-threatening
incidents affecting the coastal living. Bound by water on three sides, the flood sensitivity of coastal India largely
depends on the spatial exposure of under-equipped population groups. This spatial impact of the coastal flood
is likely to rise with the changing climate and exponential rise in the coastal population. The local governments
and stakeholders rely on spontaneous methods of coastal flood mitigation, that are temporary, and do not help
in long-term resilience.
Disaster resilience using spatial planning has been an intensely researched topic by many in this domain for
the past few decades. The development and availability of high-resolution remote sensing data and free and
open source spatial models have further facilitated the development of down-to-earth interventions for
resource-crunched developing nations. The research presents a comparative assessment of Business as Usual
Scenario (BAU) and Flood resilient scenario modelling (FResMO), emphasizing the role of spatial
planning in reducing coastal flood risk during cyclone YAAS (2021) on Sagar Island, West Bengal. In this
analysis, the flood hazard scenario of Sagar Island is developed and validated using a connected bathtub
model. The flood risk in the region is estimated as the product of various vulnerability and exposure
parameters. The vulnerability is dependent on socio-economic parameters, and exposure is related to the
spatial proximity of the region to coastal floods. The vulnerability and exposure parameters are ranked using
a multi-criteria decision using Analytical Hierarchical Process and finally integrated for estimating present
and future flood risk. The future flood risk scenario for 2030 is developed based on the built-up prediction
model ‘FUTURES’ that integrates the temporal landuse map, demography and socio-economic factors using
a multi-level logistic patch growing algorithm.
Keywords: Coastal flood risk, Flood risk modelling, FUTURES, Spatial adaptation, Vulnerability

How to cite: Nadupalli, P., Narendr, A., and Aithal, B. H.: Multi-scenario flood risk assessment: A case of Sagar Island, West Bengal, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1061, https://doi.org/10.5194/egusphere-egu23-1061, 2023.

A.79
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EGU23-576
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HS6.3
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ECS
Flood Risk Mapping in Southern Brazil using a terrain descriptor and socio-economic indices 
Deborah Dotta Correa, Pedro Luiz Borges Chaffe, and José Vinícius Boing de Souza

Floods are the most frequent natural disaster impacting society and all inhabited continents. Accurately mapping the extent of flooding is challenging as it requires a detailed, computationally demanding and data-limited representation of hydrological processes. This study focused on mapping the flood risk of the Itajaí River Basin, an important basin with an extensive history of flooding in Southern Brazil, using the HAND terrain descriptor and socio-economic indices.  A combination of three factors was used to define the flood risk: hazard, exposure and vulnerability. In order to characterize hazard, we use a parallel implementation in GPU of the HAND terrain descriptor combined with flood frequency analysis. Vulnerability was determined by combining the Human Development Index and population spatial distribution. Finally, the exposure was determined by using nightlights. Floods observed extent maps of the September 2011 50-year event in different municipalities of the Itajaí River Basin were used to determine the performance of the HAND terrain descriptor as a flood mapping tool. The best performance of the model was obtained for Rio do Sul municipality, with a correctness index of 86% and a fit index of 75%. Most of the Itajaí River Basin (93%) was classified as low risk. Of the remaining 7%, 90% was classified as medium risk, 8% as high risk and 2% as severe risk. By using the HAND terrain descriptor and socio-economic indices, a flood risk map of the Itajaí River Basin in Southern Brazil was developed which can be used as a valuable resource in urban planning, including the development of flood mitigation and response measures.

How to cite: Dotta Correa, D., Borges Chaffe, P. L., and Boing de Souza, J. V.: Flood Risk Mapping in Southern Brazil using a terrain descriptor and socio-economic indices, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-576, https://doi.org/10.5194/egusphere-egu23-576, 2023.

A.80
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EGU23-9709
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HS6.3
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ECS
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Reconstruction and optimal modelling of a flash flood in a steep Norwegian river using remotely sensed- and in-situ data 
Adina Moraru

The 2017 flash flood affecting Storelva in Utvik (West Norway), with an average slope of >10% in the ungauged reach, was reconstructed using visual observations during the event, as well as post-event field data and remote sensing. The dataset was then used for i) roughness calibration and sensitivity analysis, ii) validation of a 2D hydrodynamic model (morphodynamic data was insufficient) and reconstruction of the maximum flood extent, critical locations, and preferential flow paths, and its comparison to other modelling studies, and iii) analysis of the impact of mesh refinement on model precision for optimal model design in IberPlus.

Water levels and flow discharge were measured after the flood. The observations were used to calibrate the model in the 400m-long most downstream reach. Similarly, visual flood documentation during the event was used to model the event and validate it in the 800m-long most downstream reach.

To calibrate the model, GIS-classified wet and dry areas in the computational domain were compared with wet and dry areas observed along both banks, calculating the BIAS and RMSE for each calibration Manning. According to the sensitivity analysis, the model with Manning’s roughness coefficient of 0.065 in the upper and middle reach and 0.075 downstream showed the lowest global errors (i.e. RMSE= 1.1cm), although the numerical models generally underestimated the observed water levels (i.e. -8cm <BIAS< -1cm).

Two of the critical locations are located near bridges and the other two near a bank with very fine material, easy to erode. The preferential flow paths indicate that the erosion occurred mainly in the left floodplain. IberPlus simulated satisfactorily the observed maximum flood extent, i.e. F and C indices of 60%–87%. The results for the 2017 flood using IberPlus were compared to the (non-calibrated) hydraulics from literature using TELEMAC-MASCARET and FINEL2D. The IberPlus hydrodynamic model had the highest roughness coefficients from all the modelling studies. This might explain the significantly higher hydraulic values observed, in agreement with those obtained by the morphodynamic models. The paths preferred by the flow during the flood and the flood extent are resembling in all three models.

The F and C indices and the incremental precision between scenarios were estimated for 44,000–11.6 million cells models with uniform and variable mesh sizes. The optimal precision-gain was at model size <150,000 cells for variable mesh (R2 =0.65) versus >700,000 cells for uniform mesh (R2 >0.94), with a precision gain limited to 5–7% at best when using a finer grid. Uncertainties in the flood mapping used for validation, the hydrodynamic model set-up and input data contributed to the offset. The model precision is limited by the on-site flood protections implemented to protect private property during the flood event. These protections were effective and reduced the flood damage by 43%, yet they could not be implemented in the numerical model. Also, the model validation was carried out against a fully water-covered area, where some local dry cells were considered wet. Remotely sensed data helps understand flood dynamics and monitor flood risk in data-scarce regions.

How to cite: Moraru, A.: Reconstruction and optimal modelling of a flash flood in a steep Norwegian river using remotely sensed- and in-situ data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9709, https://doi.org/10.5194/egusphere-egu23-9709, 2023.

A.81
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EGU23-11185
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HS6.3
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ECS
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Analysis of compound flooding in the Tapi River Basin 
Zafar Beg and Kumar Gaurav

We coupled a hydrologic model Variable Infiltration Capacity (VIC) with the Hydrologic Engineering Center River Analysis System (HEC-RAS-2D) to model the compound impact of flood drivers in the Tapi River basin, India. Our modelling framework consists of two distinct phases; firstly, we calibrate and validate the VIC simulated daily stream flow of the Tapi River using the data observed at the Sarangkheda gauge (upstream of Ukai Reservoir) during the 2005-2012 and 2013-2016, respectively. Secondly, to simulate the high and low flow events, a separate HEC-RAS 2D model is forced with flood hydrograph (Ukai dam release) and stage hydrograph (Tidal level at Hazira) as upstream and downstream boundary conditions, respectively. We calibrated this hydrodynamic model for the 2012 flood event and validated it for the 2006 and 2014 flood events with the observed discharge and water level at the five gauges (Kakrapar Weir, Ghala, Kathor, Singanpur Weir and Nehru Bridge) located along the Tapi River in the Lower Tapi Basin (LTB). We observed that the VIC simulated daily stream flow accords well with the observed in-situ measurements. The Kling-Gupta and Nash Sutcliffe Efficiency values for calibration are 0.84 and 0.86, while, for validation, the values are 0.78 and 0.71, respectively. Furthermore, the hydrodynamic model analysis indicates satisfactory performance with the Root Mean Square Error (RMSE) for discharge and water levels in the range of 300-325 m3s-1 and 0.12–0.43 m, respectively. Finally, we prepare the flood hazard maps to provide critical insights for effective flood management and to enhance the flood resilience of the flood-prone regions of the LTB.

How to cite: Beg, Z. and Gaurav, K.: Analysis of compound flooding in the Tapi River Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11185, https://doi.org/10.5194/egusphere-egu23-11185, 2023.

Posters virtual: Thu, 27 Apr, 10:45–12:30 | vHall HS

Chairperson: Nick Everard
vHS.16
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EGU23-4918
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HS6.3
Flood Occurrences in Tropical Coastal Intensified by Exacerbating Extreme Weather Events 
Wenxin Zhang, Edward Park, and Xiankun Ynag

Extreme weather events attributed to global climate change brought disasters into view, the 2021-2022 Malaysian Flash Flood that crushed eight states across the peninsula astonished the world. With a death toll of 56 and total damage of $14,600,000, western Peninsular Malaysia, which has withstood acute and large amounts of precipitation in a short time, suffered the worst flood since the one that occurred in 2014. This study combined recorded sociological statistics with remote sensing data, specified the historical extreme rainfall and flash flood events since 1981 in Peninsular Malaysia, including the 2021-2022 Malaysian Flash Flood, to explicit and compare the temporal and spatial characteristics of these events. Study found since 2000s flood events occurred frequency has significantly increased, including flash floods. In addition, precipitation ditribution in Peninsular Malaysia expreienced a spread to western from concentrating in east coast. A series of factors might have exacerbated flood vulnerability of this tropical peninsular coast under the intensified extreme rainfall events in the 40 years are disscussed. 

How to cite: Zhang, W., Park, E., and Ynag, X.: Flood Occurrences in Tropical Coastal Intensified by Exacerbating Extreme Weather Events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4918, https://doi.org/10.5194/egusphere-egu23-4918, 2023.