HS6.2

EDI
Remote Sensing for Flood Dynamics Monitoring and Flood Mapping

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. Despite this evidence, our knowledge and accurate prediction of flood dynamics remain poor, mainly related to the lack of measurements and ancillary data at the global level.

In this context, remote sensing represents a value 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, 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;

- River and flood dynamics estimation from satellite (especially time lag, flow velocity, etc.)

Co-organized by NH6
Convener: Guy J.-P. Schumann | Co-conveners: Alessio Domeneghetti, Angelica Tarpanelli, Ben Jarihani, Nick Everard
Presentations
| Wed, 25 May, 15:10–18:26 (CEST)
 
Room 2.31

Presentations: Wed, 25 May | Room 2.31

Chairpersons: Guy J.-P. Schumann, Nick Everard, Ben Jarihani
15:10–15:20
15:20–15:30
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EGU22-13512
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solicited
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Highlight
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Virtual presentation
Brittany Zajic and Samapriya Roy

Flooding is the most common and costliest global natural disaster, accounting for 43% of all recorded events in the last 20 years and expected to increase the global cost of flooding tenfold by 2030. Satellite imagery has proven beneficial for numerous flood use cases from historical modeling, situational awareness and extent, to risk forecasting. The addition of high resolution, high cadence satellite imagery from Planet Labs PBC has been widely adopted by the flood community, from researchers in academia to private companies in the insurance and financial services. 

Planet Labs PBC currently operates over 200 satellites, comprising the largest constellation of Earth observation satellites. The PlanetScope dataset consists of broad coverage, always-on imaging of the entire landmass by 140+ Dove satellites at 3.7 meter resolution. Complementary to PlanetScope, the SkySat dataset includes 21 high resolution satellites imaging at .50 meter resolution with the ability to image and video any location on Earth via automated tasking commands. This presentation will highlight Planet’s capabilities serving the hydrological science community and cutting-edge flood research and technology.

How to cite: Zajic, B. and Roy, S.:  Flooding applications enabled by high resolution, high cadence imagery from the Planet Labs PBC constellation of satellites , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13512, https://doi.org/10.5194/egusphere-egu22-13512, 2022.

15:30–15:36
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EGU22-4067
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ECS
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On-site presentation
Pierre Zeiger, Frédéric Frappart, and José Darrozes

Global Navigation Satellite System Reflectometry (GNSS-R) is an emerging remote sensing technique for studying land geophysical parameters. The launch of NASA’s Cyclone GNSS (CYGNSS) mission in 2016 provides GNSS-R data in the pan-tropical area with high spatiotemporal resolution. In this study, we analyze the bistatic observations from CYGNSS for a dynamic floods detection. We compute the coherent reflectivity from CYGNSS L1 data and we grid it at a 0.1°, 7 days spatiotemporal resolution. We use a K-means clustering technique to label the CYGNSS pixels based on their time series of reflectivity. Several reflectivity patterns are extracted from the characteristics of each labelled class: low, medium or high values of reflectivity, and constant or variable amplitude throughout the year. Results are compared to static and dynamic inundation maps, elevation from digital elevation models (DEM), and to land cover information to evaluate the potential of CYGNSS observations for mapping flood dynamics at a global scale. Results highlight the influence of the presence of water on the reflected signals recorded by the CYGNSS satellites. First, high reflectivity values are found over permanent water bodies (lakes, large rivers). Then, seasonal floods are identified by a highly variable value of reflectivity throughout the year, with a peak consistent with the maximum extent of inundations. This is clearly identified over some great floodplains in the Orinoco, Amazon and Parana basins, and over irrigated croplands in the Ganges-Brahmaputra, Mekong and Yangtze basins.

While the global link between CYGNSS observations and floods is assessed, we have identified some limitations at the regional scale. First, very dense canopy layers in tropical forests reduce drastically the penetration of GNSS L-band signals, as for other microwave remote sensing data. Thus, floodplains in densely vegetated areas are underestimated using CYGNSS dataset only. Secondly, the reflectivity over bare soils as in the Sahara or in Australia is high, creating sometimes a confusion with water bodies. Soil Moisture is also well captured by CYGNSS observations with a similar seasonality and a lower amplitude of reflectivity when compared to flooded regions. Finally, CYGNSS observations are affected by the elevation. Water bodies at high elevation suffer from a reduced amplitude of the signal, but are still detectable. To overcome these limitations, a CYGNSS-based mapping of floods dynamics should integrate additional information from the biomass, the land cover and the elevation. We are currently working on this aspect to supply a 0.1°, 7 days CYGNSS flood product to the hydrological community.

How to cite: Zeiger, P., Frappart, F., and Darrozes, J.: Analysis of pan-tropical GNSS-R observations from CYGNSS satellites for floods detection and mapping , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4067, https://doi.org/10.5194/egusphere-egu22-4067, 2022.

15:36–15:42
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EGU22-1819
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Virtual presentation
David Mason, Sarah Dance, Hannah Cloke, and Helen Hooker

Urban flood mapping using SAR is an important tool for emergency flood incident management and improved flood forecasting. We have recently developed a method for detecting urban flooding using Sentinel-1 and WorldDEM data1. This is a change detection technique that estimates flood levels using pre- and post-flood images. It searches for increased backscatter in the post-flood image due to double scattering between water and adjacent buildings, compared to that in the pre-flood image where double scattering is between unflooded ground and buildings. If φ is the angle between building and satellite direction of travel, double scattering is strongest for low φ, and falls off as φ increases. It also depends on the building height and length, the depth of flooding, the roughness of the ground surface, and the complex dielectric constants of the building wall and ground surface.

Ref. 2, modelling X-band data, concluded that the increase of double scattering was only high if buildings were roughly parallel to the flight direction. The modelling assumed isolated buildings, and in a complex urban environment any increase would be further masked due to adjacent buildings. This implies a limitation in our method, since if the falloff with φ is very rapid, this could reduce the number of flooded double scatterers detected.

We used the model of ref. [3] to estimate the post- to pre-flood radar cross section (RCS) ratio for double scatterers in Sentinel-1 C-band images. In agreement with ref. [2], this predicted that high ratios would only be obtained from building walls with φ < 10°.

However, there are limitations in the models, and as a result it was decided to carry out an empirical study to examine the relationship between the RCS ratio and φ. This was based on S-1 data from the UK floods of winter 2019/2020, using flooding in Fishlake as an example of flooding in moderate housing density, and flooding in Pontypridd as an example of flooding in dense housing. A LiDAR DSM was used to allow accurate measurement of φ.

Our results showed that, as well as flooded double scatterers (DSs) with φ < 10°, a significant number of flooded DSs with 10° < φ < 30° also produced a high RCS ratio. Our method also benefited from the predilection for building houses facing south in the northern hemisphere. As the S-1 sensor is in polar orbit, descending/ascending passes image the east/west walls of a house at low φ values. Similar arguments hold in the southern hemisphere and tropics. These effects combined to provide a sufficient density of high ratio DSs from flooded buildings to estimate an accurate average flood height for a local region. In areas of high housing density, the density of high ratio DSs from flooded buildings did fall, probably due to adjacent buildings, but was still sufficient to estimate an accurate local flood height.

1 Mason et al., JARS 15(3), 032003, (2021).

2 Pulvirenti et al., IEEE TGRS, 54(30), 1532-1544. (2016).

3 Franceshetti et al., IEEE TGRS, 40(8), 1787, 1801. (2002).

 

 

How to cite: Mason, D., Dance, S., Cloke, H., and Hooker, H.: Improved urban flood mapping: dependence of SAR double scattering on building orientation., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1819, https://doi.org/10.5194/egusphere-egu22-1819, 2022.

15:42–15:48
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EGU22-2947
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ECS
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Virtual presentation
Andrei Toma and Ionut Sandric

                Rapid and accurate mapping of floods offers an excellent advantage for local, regional decision-makers to mitigate the exposure of human and material losses. The current study assessed the performance of several machine learning (ML) and deep learning (DL) models for detecting and mapping floods using Sentinel-1 SAR imagery. Three distinct approaches were used with machine learning and deep learning models: pixel classification, object-based image analysis and object instance segmentation. The ML models are Random Forest (RF) and Support Vector Machine applied for pixel classification and object-based image analysis. The DL models are U-NET, DeepLabV3 and Mask RCNN used for pixel classification and object instance segmentation. The models were implemented using SNAP (Sentinel Application Platform), ESRI ArcGIS Pro, Esri ArcGIS API for Python and Python programming language. To test the model's scalability, five different cases studies were selected. These areas are located in Romania (Prut River sector, Timiș River sector and Râul Negru sector), the United States of America (Missouri River sector) and Australia (Broughton Creek sector). Five Sentinel-1 images were used for each flood, having four collected previous to the flood event and one collected after the flood event. Each Sentinel-1 image was calibrated and ortho-corrected, and filtered using SNAP. The intensity images were stacked and scaled in the range of the intensity thresholds associated with water and non-water so that all the case studies have the same margins for intensity. Further, samples were collected in ArcGIS Pro from the Prut River region using the stack of images created from the previous step. Besides water, other classes, such as forest, agricultural fields and bare soil, were collected and used in the training process. The training for the ML models took place directly on the standardized radar images within ArcGIS Pro. The training of the DL models was done through the use of Jupyter Notebooks and ArcGIS API for Python. The models were trained on samples collected from the Prut River area and then tested on all selected regions to assess their ability to perform in different study areas. The highest accuracy, calculated as Intersect over Union, was obtained by the U-Net model (IoU score of 0.74). Comparable accuracies were obtained by the RF and SVM models implemented with OBIA, with an IoU score of 0.72. Mask R-CNN and DeepLabV3 got IoU scores of 0.70, and the lowest accuracies for floods mapping were obtained by the RF and SVM models implemented as pixel classification (both having IoU scores of 0.53).

How to cite: Toma, A. and Sandric, I.: Mapping flooded areas using Sentinel-1 radar satellite imagery series through Machine learning and Deep learning methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2947, https://doi.org/10.5194/egusphere-egu22-2947, 2022.

15:48–15:54
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EGU22-4078
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Virtual presentation
Angelica Tarpanelli, Stefania Camici, and Alessandro Mondini

Inundation is one of the major natural hazards in Europe. After a number of dramatic floods, the Member States agreed to draw up guidelines to develop a flood risk assessment, flood hazard and risk maps and flood risk management plans (Directive 2007/60/EC) with the aim to reduce the adverse consequences on the human health and the environment. Flood hazard and risk evaluation is not straightforward and it is traditionally based on hydro-monitoring systems  not adequately distributed in the territory or on hydrodynamic models as a tool for delineating flooded areas. In the last decades, the satellite sensors launched for Earth Observation represent a valid support for early warning systems and for mitigating the impact of future flooding. The ESA Earth Observation Program includes a series of satellites, Sentinels, for the operative observation of the natural phenomena and, in particular, Sentinel-1 (SAR) and Sentinel-2 (optical) are more suitable for mapping flooded areas. The two instruments assurance an almost global coverage for free. However, the spatial resolution (10 – 20 m) and the revisit time (5 – 6 days) of the sensors do not always guarantee a full mapping of inundated territories.

Here, we proposed a study to evaluate the effectiveness of the Sentinel-1 and Sentinel-2 in the mapping of floods in Europe, where the flood events have duration ranging from some hours to a few days. To reach the target, we analyzed ten years of river discharge data over almost 2000 sites in Europe and we simulated flood riverine inundations selecting flood events over three established thresholds (97th, 99th and 99.5th percentile). Based on the revisit time of both the satellites constellations and the cloud coverage for the optical sensors, we derived the percentage of potential inundation events detectable from Sentinel-1 and Sentinel-2. Assuming the configuration of a constellation of two satellites for each mission and considering the ascending and descending orbit, we find that on average the 58 % of flood events were potentially observable by Sentinel-1 and only the 28 % by Sentinel-2.

 

 

How to cite: Tarpanelli, A., Camici, S., and Mondini, A.: How many inundations are detectable in Europe using Sentinel-1 and Sentinel-2?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4078, https://doi.org/10.5194/egusphere-egu22-4078, 2022.

15:54–16:00
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EGU22-4403
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ECS
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Virtual presentation
Rosa Colacicco, Alberto Refice, Raffaele Nutricato, Annarita D'Addabbo, Davide Oscar Nitti, and Domenico Capolongo

Climate change and anthropogenic impact are intensifying the frequency and intensity of extreme flood events. This is particularly worrying in the Mediterranean area, which is highly vulnerable and therefore subject to increased flood risk. The monitoring of flooded areas at high-resolution plays an important role in all phases of disaster management, from alert to the emergency and civil protection phase, up to damage assessment, for compensation and risk reduction purposes.

This study aims at the multi-temporal analysis of remote sensing data, mainly radar data, through the implementation of a semi-automated system for the high-resolution mapping of river flooding effects. The objective is also to develop a system based on the fusion of different data sources and for different land cover types. The system includes an algorithm for the computation of multi-temporal, probabilistic flood maps, based on the analysis of amplitude series (in dB) of a stack of SAR images, acquired both in areas with permanent water and in areas with potential flooding. Exploiting a Bayesian inference framework, conditioned probabilities are estimated for the presence of water. The procedure relies on the temporal modelling of the SAR amplitudes time series, in order to account for seasonal and other slow temporal trends, and thus highlighting floods as events causing abrupt variations of the backscatter, lasting for a single or a few acquisitions. The methodology is particularly suited to data from sensors characterized by a high temporal frequency, such as the European Sentinel-1 constellation, whose two sensors acquire with the same geometrical configuration every 6 days over Europe. In parallel, a land use classification, at high resolution, is produced for each year within the period of acquisition of the satellite image stack (late 2014 to present) using Google Earth Engine [1]. This cloud-based platform makes it easy to access high-performance computing resources for processing geospatial data, allowing for the independent development of algorithms and subsequently specific applications. This supervised classification, achieved with the 'random forest' machine learning technique, is obtained through the combined use of SAR Sentinel 1 and optical Sentinel 2 images, over each entire year of interest. We show how the combination of these techniques can help gaining insight on the land cover, and on the expected changes of their appearance in the remotely sensed data in flooded conditions. This information can be used to improve the performance of the monitoring algorithm over various land cover scenarios and climatic settings.

The procedure is tested over the Metaponto plain, in the Basilicata region (southern Italy). The proposed methodologies can however be used for other contexts affected by similar events, in the Mediterranean area and worldwide.

References

  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. (2017) - Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, Volume 202, 2017, Pages 18-27, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.06.031.

How to cite: Colacicco, R., Refice, A., Nutricato, R., D'Addabbo, A., Nitti, D. O., and Capolongo, D.: High spatial and temporal resolution flood monitoring through integration of multisensor remotely sensed data and Google Earth Engine processing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4403, https://doi.org/10.5194/egusphere-egu22-4403, 2022.

16:00–16:06
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EGU22-4657
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ECS
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On-site presentation
Monika Gierszewska and Tomasz Berezowski

In this study, we investigated the influence of speckle filters and decomposition methods with different combinations of filter and decomposition windows sizes on classification accuracy. The study area was a part of Biebrza National Park, located in Northeast Poland. The C-Band SAR image from Radarsat 2 sensor was processed using various speckle filters (boxcar, IDAN, improved Lee sigma, refined Lee) in 5x5, 7x7, 9x9, and 11x11 pixel window sizes. We processed the filtered data using nine polarimetric decompositions also in 5x5, 7x7, 9x9, and 11x11 pixel window sizes. We used the calculated polarimetric features to conduct a supervised classification with random forest machine learning algorithms for each combination of processing parameters in three different scenarios: (1) each decomposition product was used separately as a model input; (2) all decomposition products with the same speckle filtering method were used as a model input; (3) all decomposition products with all speckle filtering methods were used together as the model input. Overall, the most accurate classification model (87%) was produced in scenario 3 with an 11x11 filter and decomposition windows. In scenario 1, the highest overall accuracy achieved the Cloude-Pottier decomposition (72%) and the lowest produced the Touzi decomposition (38%). In scenario 2, the IDAN filter provided the highest accuracy (84%) with an 11x11 filter window and a 9x9 decomposition window. The obtained results show that the selection of appropriate processing parameters is an important step in the SAR data classification workflow. Our study also indicates the most suitable combination of radar image processing parameters for wetland classification.

How to cite: Gierszewska, M. and Berezowski, T.: The optimal processing chain for flood mapping using polarimetric SAR in a temperate zone wetland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4657, https://doi.org/10.5194/egusphere-egu22-4657, 2022.

16:06–16:12
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EGU22-4887
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ECS
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Virtual presentation
Cinzia Albertini, Andrea Gioia, Vito Iacobellis, and Salvatore Manfreda

The use of multispectral satellite imagery for flood mapping and river monitoring is a fast and cost-effective method that can benefit from the growing availability of medium-high-resolution and free remote sensing data. Since the 1970s, several satellites are observing the Earth surface supporting water detection studies and flood management. In addition, many techniques exploiting different spectral indices have been proposed in the literature. Considering the high number of available sensors and their differences in spectral and spatial characteristics, this work aims to examine the applications of satellite remote sensing for water extent delineation and flood monitoring. Focusing on freely available optical imagery, this study presents a discussion of the most used satellites for flood and wetland mapping to highlight trends of current research studies. Furthermore, performances of the most common spectral indices for water segmentation are analysed first qualitatively, based on evidence obtained from a significant literature review, and then quantitatively by comparing different water-related index algorithms applied to a real case study. Performance assessment is carried out to provide an overview of the best sensor-specific spectral index in detecting surface water and expressed in terms of overall accuracy (OA) and Kappa coefficient.

How to cite: Albertini, C., Gioia, A., Iacobellis, V., and Manfreda, S.: Surface water detection and flood mapping using optical remote sensing and water-related spectral indices, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4887, https://doi.org/10.5194/egusphere-egu22-4887, 2022.

16:12–16:18
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EGU22-4942
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Virtual presentation
Jie Zhao, Yu Li, Patrick Matgen, Ramona Pelich, Renaud Hostache, Wolfgang Wagner, and Marco Chini

Synthetic Aperture Radar (SAR)-based floodwater detection in urban areas remains challenging because of the complex urban environment. Generally, open water appears as dark in SAR intensity images due to low values of backscattering caused by specular reflections, while standing water in built-up areas may lead to an increase of the backscattering value depending on the strength of the double bounce effect between the floodwater and the building’s facades. According to previous studies, it is known that the multitemporal interferometric SAR coherence is valuable for improving flood detection in urbanized areas while SAR intensity is more suited to map floods over bare soil. Deep convolutional neural networks approaches have also shown promising results in remote sensing applications, such as land cover classification, object detection and floodwater mapping. For the latter case and with particular attention to urban areas, there is not yet a well-established and unique method neither a privileged dataset to perform the detection of floodwater. In order to have a better understanding of the ability of different deep learning models for urban flood mapping, we compared the performance of three different deep learning-based methods, i.e. U-Net, U-Net with convolutional block attention module (CBAM) and U-Net with an Urban-aware module developed by us, for large-scale urban flood mapping. Here, we used as input multi-temporal intensity and interferometric SAR coherence data and the classification differentiates between flooded bare soils/sparely vegetated areas and flooded urban areas. To learn how to focus on different inputs, the urban-aware U-Net considers prior information provided by a SAR-derived probabilistic urban mask while CBAM U-Net only uses annotated data.
The annotated training dataset is composed of a small subset of Sentinel-1 data acquired during the Houston (US) flood, caused by Hurricane Harvey in 2017, and the Iwaki (Japan) flood, caused by Typhoon Hagibis in 2016, where ground truth data are available. Three independent datasets (i.e. Houston (US) flood in 2017, Koriyama (Japan) flood in 2016 and Beira (Mozambique) flood in 2019) were considered as test cases in order to evaluate the generalizability capabilities of the proposed approach with respect to different scenarios. To evaluate the accuracy of flood mapping in urban areas, we adopted the F1 score. The urban-aware U-Net improves the F1 score to 0.63 in the Houston case and 0.66 in the Beira case while the other two models’ results have quite low F1 values (0.04 ~ 0.38) in Houston case and Beira case. Moreover, a visual inspection of the resulting floodwater maps over the entire Sentinel-1 frame suggests that urban-aware U-Net has less over-detection compared with U-Net and CBAM U-Net. These results indicate that the prior information helps in the proper use of multi-temporal SAR data in large-scale flood mapping. Moreover, considering that the models were trained using a very small and independent dataset and given the agreement of the results with the available ground truth, we consider urban-aware U-Net as a promising approach, having the potential to be used for near real-time urban flood mapping in case of future flood events.

How to cite: Zhao, J., Li, Y., Matgen, P., Pelich, R., Hostache, R., Wagner, W., and Chini, M.: A Comparison of three deep learning-based methods for large-scale urban flood mapping using SAR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4942, https://doi.org/10.5194/egusphere-egu22-4942, 2022.

16:18–16:24
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EGU22-5877
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ECS
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Virtual presentation
Qin Wang, Lu Zhuo, Miguel Rico-Ramirez, Dawei Han, Jiao Wang, Ying Liu, and Sichan Du

Flood events are expected to become increasingly common with the global increases in weather extremes. 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) sensors represent an indispensable data source for flood disaster planners and responders, given their ability to image the Earth's surface nearly independently of weather conditions and the time of day or night. 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 all 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 unexpected floods in near real-time. Additionally, historical Landsat-based surface water class probabilities are used to distinguish unexpected floods from permanent or seasonally occurring surface water. 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., Rico-Ramirez, M., Han, D., Wang, J., Liu, Y., and Du, S.: Flood inundation mapping using Sentinel-1 SAR images with Google Earth Engine cloud platform, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5877, https://doi.org/10.5194/egusphere-egu22-5877, 2022.

16:24–16:30
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EGU22-9031
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Presentation form not yet defined
Guy J.-P. Schumann, Laura Giustarini, and Paolo Campanella

Since the early seventies, it has been known that satellite images can add value to mapping and monitoring floods. With the early launches of the Landsat series, followed in the early eighties and nineties by synthetic aperture radar (SAR) missions on SIR-B, ERS-1 and RADARSAT-1 with their all-weather and day and night capabilities considerably expanded the potential of flood mapping from space. Since then, the world of open-access Earth Observation (EO) has grown considerably and available data to inform about floods and assist flood disasters from local to global scales have proliferated.

This EO data proliferation coupled, in recent years, with complementing data from drones, IOT sensors and significant progress in online cloud computing platforms and interoperability has led to a massive amount of progress in both geospatial technology development and better actionable products and services based on EO. In the context of floods, machine learning has started to enable onboard satellite mapping, and reconstructing flooded area under cloud cover in optical images. In addition, recent scientific progress in SAR signal coherence processing is enabling the mapping of flooded buildings in urban areas. Online cloud computing platforms can now be used to upscale such flood mapping applications over entire regions, countries or even continents with the click of a button.

Using several use case illustrations, this paper will present some major historical breakthroughs in EO-based flood mapping before presenting recent technological advances in rapidly mapping rural and urban flooding across various spatial scales. 

How to cite: Schumann, G. J.-P., Giustarini, L., and Campanella, P.: Advances in online computing platforms and satellite sensor technologies enable unprecedented uptake of EO products, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9031, https://doi.org/10.5194/egusphere-egu22-9031, 2022.

16:30–16:36
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EGU22-6019
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ECS
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Virtual presentation
Mostafa Rashidpour, Mahdi Motagh, Karim Solaimani, Mohammadali Hadian Amri, Sigrid Roessner, and Kaka Shahedi

Knowledge about the location and extent of flooded areas in large catchments with different rainfall- runoff response in each sub-catchment is of key importance for planning flood management strategies. Haraz catchment with an area of more than 4000 square kilometers is located in the north of Iran and is frequently affected by floods. The lack of reliable spatiotemporal information on flood occurrence has been the main limiting factor for assessment of flood hazard and risk in this catchment.

The current availability of satellite remote sensing sensors with high spatial and temporal resolution is highly valuable for detailed analysis of individual flood occurrence across various scales. In this study, we develop a machine learning approach using data from various remote sensing sensors including Landsat, Planet and Sentinel-2 to detect flood events in different tributary areas within the Haraz catchment which have occurred between 2015 and 2021. The random forest algorithm implemented in Google Earth Engine was used for image classification before and after flood events. The areas of each landcover type inundated by flood waters were calculated for the single flood events and the binary flood masks were overlaid on the study area. The results have revealed that seven flood events could be detected, whereas the two events in April 2015 and April 2019 had led to the largest areas of inundation because of the nature of these floods as riverine flood. Moreover, we have found that two parts of the river network – one in middle part of Norroud subcatchment adjacent to Baladeh City and another one in the area of the catchment outlet - have the largest potential for flood risk because of the frequency of inundation and the high vulnerability of built-up areas that occupy the floodplain. Thus, the findings of this study form the basis for a better understanding of the characteristics for recent flood hazard and risk in Haraz catchment.

How to cite: Rashidpour, M., Motagh, M., Solaimani, K., Hadian Amri, M., Roessner, S., and Shahedi, K.: Flood analysis using satellite imagery and machine learning within Google Earth Engine: A catchment-based study in Northern Iran, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6019, https://doi.org/10.5194/egusphere-egu22-6019, 2022.

Coffee break
Chairpersons: Guy J.-P. Schumann, Nick Everard, Ben Jarihani
17:00–17:06
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EGU22-11555
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Virtual presentation
Marco Chini and the Global Flood Monitoring team

It is expected that climate change – combined with a growing global population in ill-planned flood-prone coastal and riverine areas – will increase the destructive potential of river floods. Central to inundation risk mitigation are the acquisition and processing of high resolution and high frequency information on river discharge response to precipitation. To address this pressing societal need, we introduce a global scale satellite Earth Observation-based flood mapping and forecasting service – capitalizing on the quasi-continuous data stream generated by the radar onboard the Sentinel-1 satellite. Radar signals emitted from satellites are a very powerful tool for assessing flood extents – capable of ‘seeing’ through cloud covers and covering almost instantaneously thousands of square kilometers. In order to rapidly translate the large volume of SAR data into floodwater maps and value adding services, the European Commission’s Joint Research Centre (JRC) recently added Global Flood Monitoring (GFM) products based on Sentinel-1 as a new component to its Copernicus Emergency Management Service (CEMS). The GFM products are obtained by processing all incoming Sentinel-1 SAR images within 8 hours after data acquisition.  To reach a high degree of automation, the system takes advantage of the constantly updated 20 m Sentinel-1 data cube made available by the Earth Observation Data Centre (EODC) facilities. It is requisite that the Sentinel-1 based retrieval algorithm, as one of the core components of GFM, is both efficient and robust. Moreover, it is designed to balance two objectives:  to detect water at high accuracy (i.e. permanent and seasonal water bodies, and floodwater), while minimizing the identification of false alarms due to water-look-alikes surfaces that can be confused with floodwater. To enhance the robustness of the system, an ensemble-based mapping algorithm is implemented, which combines three independent floodwater mapping algorithms driven by different approaches. 1) LIST’s algorithm that requires three main inputs: the most recent SAR scene to be processed, a previously recorded overlapping SAR scene acquired from the same orbit and the corresponding previously computed flood extent map. The change detection algorithm maps all increases and decreases of floodwater extent and makes use of this information to regularly update the flood extent maps. 2) DLR’s algorithm requires one scene as the main input and further exploits three ancillary raster datasets: i.e. a digital elevation model (DEM), areas not prone to flooding and a reference water map. 3) TU Wien’s algorithm requires three input data sets: i.e. the SAR scene to be processed, a projected local incidence layer, and the corresponding parameters of a previously calibrated multitemporal harmonic model. The final floodwater map is obtained by integrating the results of the three independently developed algorithms. Pixelwise flood classifications are based on majority voting, such that at least two algorithms are in agreement. The algorithm is currently being extensively tested for different regions all over the world. A first quantitative evaluation shows encouraging results in relation to the accuracy for delineating the evolution of water bodies and further improvements to increase the accuracy of the GFM product is ongoing. 

How to cite: Chini, M. and the Global Flood Monitoring team: An ensemble-based approach to map floods globally using Sentinel-1 data: The Global Flood Monitoring system of the Copernicus Emergency Management Service, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11555, https://doi.org/10.5194/egusphere-egu22-11555, 2022.

17:06–17:12
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EGU22-11964
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ECS
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Presentation form not yet defined
Using MODIS thermal data for mapping and monitoring of a massive multi-year flooding event in South Sudan for humanitarian response and decision making 
(withdrawn)
Sebastian Boeck, Rogerio Bonifacio, Lia Pozzi, and Paulina Bockowska
17:12–17:22
17:22–17:32
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EGU22-8645
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solicited
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Highlight
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On-site presentation
Federica Remondi, David Schenkel, and Rogier de Jong

Flooding has been consistently one of the most recurrent and costly natural catastrophes globally. Only in 2021 large flood events claimed more than 2 000 victims and caused over USD 75 billion of economic losses, of which a quarter was covered by the insurance sector.
Modelling floods and simulating their impact have proven to be particularly challenging in locations with fine-scale changes in elevation, complex terrains and man-made structures as is typical for dense urban centres.  By partnering with ICEYE, the largest commercial synthetic-aperture radar satellite operator, Swiss Re aims to advance flood risk modelling, assist disaster response and provide enhanced insights and new products to its clients. 

We present few applications for the re/insurance sector of the remotely acquired flood maps at high resolution and water depth estimations. Firstly, the flood footprints are provided to clients for assessing the event magnitude and enabling faster loss assessment and payouts. Secondly, they are used as input to flood catastrophe models to obtain a first loss estimation for reinsurance portfolios. Thirdly, new insurance products that rely on the remotely-sensed flood footprints to trigger a payout have been explored. These parametric flood insurances, even with their limitations, present relevant potential applications for cities, large regions and the agriculture sector.

How to cite: Remondi, F., Schenkel, D., and de Jong, R.: Novel usage of remotely-sensed flood footprints in the re/insurance sector, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8645, https://doi.org/10.5194/egusphere-egu22-8645, 2022.

17:32–17:38
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EGU22-3724
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ECS
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On-site presentation
Yinxue Liu, Paul Bates, and Jeffery Neal

Terrain representation is important in many fields including flood mapping. In urban areas, topography data without ground objects are preferred in flood simulation for multiple concerns. However, the topography data collected by remote sensing techniques all contain the artefacts height to some extent. High-resolution photogrammetry DEMs, like ArcticDEM, are emerging with the widely available possibility while approaches to generate bare-earth DEM from them has yet been fully investigated. In this paper, we used the city of Helsinki as a case study. The optimal filter was selected among two morphological filters (PMF, SMRF) and then was used to generate bare-earth ArcticDEM with its various parameter combinations, generating a filtered ArcticDEM ensemble. Then, the elevation error and the flooding performance for a pluvial flooding scenario of this ensemble were evaluated at 2 m and 10 m resolution, respectively, using the LIDAR DTM as the benchmark. The SMRF was found to be advantageous over PMF and be effective at removing artefacts with broad parameter range. In the optimal ArcticDEM-SMRF the RMSE was reduced by up to 70%, achieving 1.02 m, and the simulated water depth error was reduced to a comparable magnitude expected from the LIDAR DTM simulation of 0.3 m. This paper indicates that the SMRF can be directly applied to generate bare-earth ArcticDEM in urban environment although caution should be taken when using in areas with densely packed buildings or vegetation. The results imply that the high-resolution photogrammetry DEMs have the potential to be an alternative of LIDAR in the future.

How to cite: Liu, Y., Bates, P., and Neal, J.: Bare-earth DEM generation from ArcticDEM, and its use in flood simulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3724, https://doi.org/10.5194/egusphere-egu22-3724, 2022.

17:38–17:44
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EGU22-3787
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ECS
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Virtual presentation
María Navarro-Hernández, Javier Valdés-Abellán, Roberto Tomás, Serena Tessitore, Pablo Ezquerro, and Gerardo Herrera

Floods are natural extreme events that occur after heavy rains, having a great impact on human settlements developed along flood risk areas (such as floodplains, valleys, etc.). Alto Guadalentin Valley is an orogenic tectonic depression affected by extreme flash floods. Additionaly, this area is affected by the fastest subsidence in Europe with a rate up to -10 cm/year due to groundwater withdrawal. In this study we present two flood event 2-D models comparison between different time land subsidence scenarios (1992 and 2016). The flood inundation modelling was performed in the Alto Guadalentin River and their tributaries using the Hydrologic Engineering Center River Analysis System 2D (HEC-RAS 2D) model, for the purpose of determining the flooded area extent and the depth water variations produced by the effect of land subsidence over time. To recreate both scenarios, different sets of synthetic aperture radar (SAR) images acquired by ERS (1992-2000), ENVISAT (2003-2010) and Cosmo-Skymed (2011-2016) satellites were used to calculate the magnitude and  spatial distribution of land subsidence using SAR Interferometry (InSAR) technique. The subsidence accumulated between 1992 and 2009 and between 2009 and 2016 derived from InSAR was substracted and added, respectively, to a Digital Surface Model (DSM) with 2.5 m spatial resolution from 2009 obtained using Light Detection and Ranging (LiDAR) to obtain the actual topography of the valley before (i.e. 1992) and after (i.e. 2016) the subsidence period covered by InSAR. These DEMs were used to generate the two 2D hydraulic models that ran in an unsteady mode. The results revealed significant changes in the water surface elevation with an increase of 3,073,200 m2 in the areas with depth water greater than 0.8 m over 24 years. From these simulation a flood risk map was performed. The resulting flood hazard data provides useful information to understand the inundation risk taking into account land subsidence contribution in the Alto Guadalentin Valley. This information can be of paramount importance for emergency management and civil protection against future potential floodings.

How to cite: Navarro-Hernández, M., Valdés-Abellán, J., Tomás, R., Tessitore, S., Ezquerro, P., and Herrera, G.: Flood inundation mapping using 2-d streamflow hydraulic modeling and land subsidence data from InSAR observations in the Alto Guadalentin valley, Spain., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3787, https://doi.org/10.5194/egusphere-egu22-3787, 2022.

17:44–17:50
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EGU22-721
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On-site presentation
Helen Hooker, Sarah L. Dance, David C. Mason, John Bevington, and Kay Shelton

Flood inundation forecast maps provide an essential tool for disaster management teams to aid planning and preparation ahead of a flood event in order to mitigate the impacts of flooding on the community. Evaluating the accuracy of forecast flood maps is essential for model development and improving future flood predictions and can be achieved by comparison with flood maps derived from remote-sensing observations. Conventional, quantitative binary verification measures typically provide a domain averaged score, at grid level, of forecast skill. This score is dependent on the magnitude of the flood and the spatial scale of the flood map. Binary scores have limited physical meaning and do not indicate location specific variations in forecast skill that enable targeted model improvements to be made. A new, scale-selective approach is presented to evaluate forecast flood inundation maps against Synthetic Aperture Radar (SAR)-derived observed flood extents. We evaluate forecast flood maps out to 10-days lead time for the Rivers Wye and Lugg (UK) during Storm Dennis, February 2020. A neighbourhood approach based on the Fraction Skill Score is applied to assess the spatial scale at which the forecast becomes skilful at capturing the observed flood. This skilful scale varies with location and when combined with a contingency map creates a novel categorical scale map, a valuable visual tool for model evaluation and development. The impact of model improvements on forecast flood map accuracy skill scores are often masked by large areas of correctly predicted flooded/unflooded cells. To address this, the accuracy of the flood-edge location is evaluated: this provides a physically meaningful verification measure of the forecast flood-edge discrepancy. Representation errors are introduced where remote sensing observations capture the flood extent at different spatial resolutions in comparison with the model. We evaluate the sensitivity of the verification measures to the resolution of the SAR-derived flood map.

An ensemble of forecast flood inundation maps has the potential to represent the uncertainty in the flood forecast and provides a location specific, probabilistic, likelihood of flooding. This gives valuable information to flood forecasters, flood risk managers and insurers and will ultimately benefit people living in flood prone areas. We apply a scale selective approach to evaluate the spatial predictability of forecast ensemble flood maps. An ensemble forecast of flooding of the Brahmaputra in the Assam region, August 2017, is evaluated using flood extents derived from Sentinel-1 SAR images. The results are presented on a Spatial Spread-Skill (SSS) map, indicating where the flood map ensemble is over-, under- or well-spread. Overall, emphasis on scale, rather than domain-average score, means that comparisons can be made across different flooding scenarios and forecast systems and between forecasts at different spatial scales.

How to cite: Hooker, H., Dance, S. L., Mason, D. C., Bevington, J., and Shelton, K.: Spatial scale evaluation of forecast flood inundation maps using Synthetic Aperture Radar (SAR) images., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-721, https://doi.org/10.5194/egusphere-egu22-721, 2022.

17:50–17:56
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EGU22-294
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On-site presentation
Mohamed Hamouda, Gilbert Hinge, and Mohamed Mohamed

In recent years, many researchers indicated that earth-observing satellites perform well in measuring or estimating precipitation rates. However, it has been highlighted that the performance of satellite rainfall estimates (SREs) is affected by many factors. In this study, a meta-data analysis was conducted to assess the performance of different SREs for flood and drought monitoring under diverse settings to test the influence of factors related to climate, topography, watershed size, and length of SREs data record. Koppen climate classification was used to classify the different studies into different climatic zone. Mean elevation was used as an indicator of varying topography. Studies were grouped into three different categories depending upon their available data record length. The impact of various factors on the performance of SREs was assessed with three statistical indices: Pearson correlation coefficient, Root Mean Square Error, and Nash-Sutcliffe Efficiency. Results showed that the performance of SREs for drought and flood monitoring is influenced by the climate, length of the data record, interactions between the applied hydrological model and type of SRE, and topography. Microwave-based SREs performed were found to perform better than infrared-based SREs. Low lying landscapes exhibited higher accuracy of SREs in flood and drought monitoring compared to complex mountainous terrain. In most cases, IMERG and CMORPH outperformed other SREs.  IMERG showed the best drought monitoring performance with Pearson correlation values ranging between 0.96-0.99. It was found that the best SREs that can represent the observed streamflow vary depending on the type of hydrological models. Also, the hydrological model performance for flood prediction significantly increases (p<0.05) when using the SREs for model calibration compared to when the model is manually calibrated with historical gauge data. Bias-adjusted SREs performed better than their counterpart. Overall, SREs offer great potential for flood and drought monitoring, but their performance needs to be enhanced for hydrological applications.

How to cite: Hamouda, M., Hinge, G., and Mohamed, M.: Performance of satellite rainfall estimates for flood and drought monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-294, https://doi.org/10.5194/egusphere-egu22-294, 2022.

17:56–18:02
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EGU22-4556
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Presentation form not yet defined
Michel Wortmann, Louise Slater, Richard Boothroyd, Greg Sambrook Smith, and Jeffrey Neal

The conveyance capacity of rivers is a key uncertainty in regional and global flood models. Most models resort to assumptions of uniform discharge recurrence of 1-2 years, using modelled discharge. While this assumption may hold on average, reach-scale bankfull discharge has been shown to vary significantly at the global scale. To improve this key boundary condition in large-scale hydrodynamic models, we have coupled emerging understanding of the hydrological and geomorphological drivers of bankfull discharge with recent advances in remote sensing products and machine learning. Using measured bankfull discharge values derived from stage-discharge and width-discharge relationships as reference, we construct a data-driven model to estimate bankfull discharge globally at the reach scale (30m centreline pixels and sub-kilometre vector reaches). Various remote sensing products are used as predictor variables that pertain to either catchment-wide or reach-specific attributes. This includes river geometry and floodplain metrics derived from Landsat water masks that have also been used to construct the underlying river network. This novel river network was designed to be as DEM-independent as possible, allowing for multi-thread channels, bifurcations and canals. Early results indicate good agreement between predicted and independent reference values.

The new dataset will be used to improve the parametrisation of a state-of-the-art global flood model as part of the EvoFlood research project (NERC, UK), but is also expected to be useful for other hydrological and hydrodynamic models as well as investigations at regional to global scales.

How to cite: Wortmann, M., Slater, L., Boothroyd, R., Sambrook Smith, G., and Neal, J.: Observation-based bankfull discharge estimates to improve global flood models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4556, https://doi.org/10.5194/egusphere-egu22-4556, 2022.

18:02–18:08
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EGU22-9345
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ECS
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Virtual presentation
Margherita Bruscolini, Taiwo Ogunwumi, and Guy Schumann

The Niger River and floodplain landscape is experiencing a constant change as a result of natural and human processes thereby contributing to the yearly occurrence of flooding. The increasing flood frequency and intensity causes loss of life, destruction of assets and disrupts the livelihood of a large proportion of the population. Due to the current data challenges and lack of hydrological information we are developing a 2-D flood inundation model showing the spatially distributed dynamics of water surface elevation and future flood extent of Niger river and its surroundings. We considered the following parameters such as floodplain topography, river channel widths, banks heights, model parameters, and hydrology information to develop our final output which is an interactive web visualization map showing the inundated extent. Our developed 2D flood prediction model can be extended to other parts of the Niger River Basin which will contribute to a positive regional economic and environmental impact. It will also help the relevant ministries, emergency institutions, local partners and national government of Niger to build safe and resilient communities through effective risk communication and contribute to the achievement of the Sustainable Development Goal (SDG) 11 and 13.

How to cite: Bruscolini, M., Ogunwumi, T., and Schumann, G.: Space-Enabled Modeling of the Niger River to Enhance RegionalWater Resources Management, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9345, https://doi.org/10.5194/egusphere-egu22-9345, 2022.

18:08–18:14
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EGU22-11696
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On-site presentation
Jose Maria Bodoque del Pozo, Estefanía Aroca Jiménez, Miguel Ángel Eguibar Galán, and Juan Antonio García Martín

Digital surface models (DSMs) play a critical role in obtaining reliable flood hazard maps for urban areas. Widespread availability of LiDAR data (where available) greatly facilitates obtaining geometrically sound DSMs. However, to date, insufficient attention has been paid to generating methodological approaches to obtain geometrically consistent DSMs. Here, we propose an application-oriented protocol to obtain a geometrically robust DSM (DSM1 hereafter). Additionally, two further DSMs were produced considering, firstly, depiction of streets using breaklines as ancillary information (DSM2) and, secondly, direct interpolation of LiDAR data (DSM3). Geometric robustness of these DSMs was evaluated qualitatively, by plotting longitudinal profiles and cross sections to dominant runoff pathways, as well as quantitatively, through assessing DSMs vertical accuracy. We also assessed impact on hazard maps depending on geometric consistency of DSMs employed. To do so, hydraulic outputs resulting from DSM1 were used as a benchmark to compare hydraulic outputs obtained from DSM2 and DSM3. This comparison was made at two spatial resolution levels: i) considering total area flooded in each case through determining the F statistic; and ii) at the level of each pixel by calculating the kappa statistic from a confusion matrix. Our results revealed that: 1) DSM1 defined geometrically consistent configurations for main runoff pathways; 2) in urban areas with higher street and building density DSM1 provided better vertical accuracies than DSM2 and DSM3; and 3) reliability of flood hazard maps strongly depend on geometric quality of the DSMs produced. Findings deployed here, might be very valuable in achieving further reduction and better flood risk management.

How to cite: Bodoque del Pozo, J. M., Aroca Jiménez, E., Eguibar Galán, M. Á., and García Martín, J. A.: Application-Oriented Methods for Obtaining Geometrically Robust Digital Surface Models for Flood Hazard Assessment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11696, https://doi.org/10.5194/egusphere-egu22-11696, 2022.

18:14–18:20
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EGU22-12538
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ECS
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Presentation form not yet defined
Shahrazad Abu Ghazleh, Abdallah Al Bizreh, and Ingo Sass

During the last three decades, six major floods have stricken Al Ain city, UAE, caused serious property loss. Therefore, morphometric and hydrologic characteristics of Hafit mountain basin / Al Ain city have been investigated using GIS and remote sensing. This investigation helped to determine the main factors controlling flood hazard in Al Ain city and the most affected area by flood hazard.

Watershed analysis of the study area helped to identify five main sub-basins. All of them are drained to the west as they are influenced by the surface topography and dipping slopes. This analysis explains the abundance of surface and groundwater west of Hafit Mountain.

Five pour points have been placed on the lowest point of each basin where the highest accumulation flow ratio occurs. Another pour point was identified where a big change in stream direction occurred. These pour points are considered the most threatened areas by flood hazard and consequently potential sites for building dams and stream gauges. The dams and gauges could be also used to recharge exploited groundwater aquifer that contribute significantly to sustainable water resource management in such a hyper-arid area.

The highest flow accumulation occurs in the northwestern part of Wadi Al Ain up to 140 km2, which explains the re-occurrence of flood in Al Ain City for several years.

How to cite: Abu Ghazleh, S., Al Bizreh, A., and Sass, I.: Hydrological Analysis and Flood Hazard Mitigation in Al Ain City, United Arab Emirates (UAE), SE Arabia: GIS and Remote Sensing Implication, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12538, https://doi.org/10.5194/egusphere-egu22-12538, 2022.

18:20–18:26
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EGU22-12867
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Presentation form not yet defined
Antonio Annis, Fernando Nardi, and Fabio Castelli

High resolution flood forecasting models integrated in Early Warning Systems (EWSs) can be supported by traditional (e.g., stage gauges) or innovative (e.g., Earth Observation – EO - data) sensors as inputs or observations for model calibration/validation or data assimilation. Stage gauges provide information only in specific points along the river network and could fail during extreme events. On the other hand, EO data could have strong limitations due by their spatial and temporal resolution, especially at medium-small scales. Therefore, multiple sources of distributed flood observations could represent a solution for managing uncertainties of flood models and lack of information left by each sensor.

In this work, a flood modelling approach is proposed for the joint assimilation of both water level observations and EO-derived flood extents. The assimilation approach implements a Ensemble Kalman Filter, whose forecasting model includes a parsimonious geomorphic rainfall-runoff algorithm (WFIUH) and a Quasi-2D hydraulic algorithm. To overcome stability issues related to the updating of the Quasi-2D hydraulic model, novel approaches are proposed to both assimilate multiple stage gauge observations and retrieve distributed observed water depths from satellite images. The flood modelling chain is tested both separately and jointly assimilating stage gauges and satellite derived flood extents on a flood event for the Tiber river basin in central Italy. Results reveal that the assimilation of observations from static sensors and satellite images led to an overall improvement of the simulation performances in terms of Nash-Sutcliffe efficiency Pearson correlation and Bias to the Open Loop simulation. Moreover, the joint assimilation of the abovementioned observations allowed to reduce the flood extent uncertainty as respect to the disjoint assimilation simulations for several hours after the satellite image acquisition.

How to cite: Annis, A., Nardi, F., and Castelli, F.: Testing the performance of a near-real time flood mapping framework jointly assimilating water levels from river gauges and satellite flood maps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12867, https://doi.org/10.5194/egusphere-egu22-12867, 2022.