HS6.2

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.