The socio-economic impacts associated with floods are increasing. According to the International Disaster Database (EM-DAT), 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 (2006–2015), while the overall economic damage is estimated to be more than $300 billion. Despite this evidence, and the awareness of the environmental role of rivers and their inundation, 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. 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;
- 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.).
vPICO presentations: Thu, 29 Apr
How to cite: Kalaitzis, F., Mateo Garcia, G., and Marchisio, G.: Water monitoring with Very High Resolution satellite imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10295, https://doi.org/10.5194/egusphere-egu21-10295, 2021.
Compound flooding (CF), as a result oceanic, hydrological, meteorological and anthropogenic processes, is an extreme event that threatens life and assets of people living in low-lying areas worldwide. Large-scale CF is often studied with hydrodynamic models that combine either successive or concurrent processes to simulate flood dynamics. In recent years, convolutional neural networks (CNNs) and data fusion (DF) techniques have emerged as feasible and simple alternatives for post-flood mapping when compared to complex modeling. Yet, both techniques have not been explored for large-scale CF mapping. Here, we evaluate the performance of a CNN & DF framework for generating CF maps driven by Hurricane Matthew that hit the southeast Atlantic coast of the U.S. in October, 2016. The framework fuses multispectral imagery (Landsat ARD), dual-polarized synthetic aperture radar data (SAR) and coastal digital elevation maps (DEMs) to generate flood maps of moderate (30 m) spatial resolution. We first train/validate the CNN & DF framework with official land cover maps (C-CAP) as well as flood maps obtained from a calibrated Delft3D-FM model of Savannah River estuary in Georgia, and then evaluate the framework in the southeast Atlantic coast. The highest overall accuracy (97%) and f1-score for permanent/flood water classes (99/100%) are achieved when ARD, SAR and DEM datasets are readily available and adequately fused. Moreover, the resulting CF maps agree well (80%) with hindcast surge and flood guidance maps of the Coastal Emergency Risk Assessment (CERA) web mapper. We also evaluate the framework with different DF alternatives and highlight its usefulness for large-scale compound flood hazard assessments and a thorough calibration of hydrodynamic models. Future work is envisioned toward a comprehensive CNN & DF framework that provides not only accurate large-scale flood extent maps, but also inundation depth based on both deep learning and multi-source data fusion.
How to cite: Muñoz, D. F., Muñoz, P., Moftakhari, H., and Moradkhani, H.: Large-scale compound flood mapping with deep learning and data fusion techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1454, https://doi.org/10.5194/egusphere-egu21-1454, 2021.
The German Aerospace Center and NASA's joint mission, the Gravity Recovery and Climate Experiment (GRACE) operational from 2002 until October 2017, provided measurements of Earth's gravity field anomalies. Its follow-on mission GRACE-FO, implemented by NASA and GFZ, was launched in May 2018 and continued to give us large-scale measurements of the Earth's gravity variations. These variations in gravity are used to determine anomalies of total water storage (TWSA) which can provide us with insights into global water redistribution on a monthly up to a daily basis.
Most common natural disasters that still require efficient early warning systems are floods. Floods are causing significant economic and humanitarian losses on a global scale and are triggered by the interaction of different hydro-meteorological processes (e.g. precipitation, sub-surface water storage, snow cover).
We aim to explore GRACE and GRACE-FO products' possibilities to detect the water storage dynamics associated with floods in large river catchments. We include analysis of the basins' wetness states before the flood events, which eventually can give us early indicators of flood development. During the GRACE data period, we investigate around 2500 historical floods from the Dartmouth Flood Observatory (DFO). We acquire GRACE data with daily resolution from the latest releases of ITSG and GFZ for the spatial extent of DFO floods and reduce TWSA values by long-term trends and by average seasonal variability. Furthermore, we assess the available river discharge time series, during the GRACE period, obtained from the Global Runoff Data Centre (GRDC) for the flood event separation. We compare GRACE-based water storage anomalies to flood events' characteristics, like peak, volume, and duration. Results show the potential of GRACE-based TWSA to detect large-scale flood events.
How to cite: Latinovic, M., Güntner, A., Flechtner, F., Murböck, M., and Kwas, A.: Global flood monitoring with GRACE/GRACE-FO, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2683, https://doi.org/10.5194/egusphere-egu21-2683, 2021.
Synthetic Aperture Radar (SAR) based flood maps are rapidly becoming a vital part of flood monitoring applications, since they provide unobscured observations independent of illumination or weather conditions. As water surfaces are physically smoother than microwave wavelengths, they appear dark in SAR imagery due to specular reflection, enabling the automatic delineation of flooded areas. However, in arid regions using backscatter thresholds to identify inundation results in numerous false positives, since dry and smooth desert sand appears as dark as water in SAR images. Accordingly, a novel Sentinel-1 SAR-based flood mapping algorithm S1-L1 to discern flood inundation from water lookalike surfaces in arid regions. The swath is tiled to ensure comparable land-water pixel distributions and long-term water recurrence records from optical Landsat sensors is used to classify potentially water and definitely land (DL) areas. Smooth surfaces and radar shadow regions, which exhibit backscatter lower than the median value for >50% of the preceding year, are excluded from the DL pixels to avoid thin long tailed distributions. The first percentile value of the DL distribution is selected as the water threshold for each band (VV and VH), to include the maximum possible water pixels without letting in large volumes of land pixels. A Gaussian contextual smoother is used to combine the individual layers into the binary flood mask, with a weighted combination of the layers computed based on the underlying land-use. An empirical sensitivity analysis showed that different low backscatter frequency thresholds work better in different regions, and thus, a fuzzy flood plausibility layer (FPL) is proposed as a post-processor. The FPL improves upon the current state-of-the-art sand exclusion layers (SELs) by combining distance from drainage with seasonally dark surfaces and shadows identified through annual SAR backscatter time series analysis. Additionally, known agricultural land-use areas with low values of Sentinel-2 based Soil Adjusted Vegetation Index (SAVI) are used to identify harvested croplands. S1-L1 was evaluated using (1) expert classified Sentinel-1 SAR-based flood maps and (2) with Sentinel-2 clear view coincident optical maps for the 2020 flood events in Ghana (September) and Republic of the Congo (November). S1-L1 performance is compared to (a) Otsu thresholding (liberal and conservative) and (b) a deterministic SEL with >60% low backscatter frequency, to assess improvements over current best performing approaches for arid areas. First results demonstrated 50% false positive reductions over traditional Otsu approaches and consistent improvements of >20% in Critical Success Index values. Findings indicate that S1-L1 has the potential to efficiently differentiate between water and lookalike regions, and can facilitate more reliable SAR-based flood mapping in deserts.
How to cite: Dasgupta, A., Goodman, M., Yague Martinez, N., and Tellman, B.: Improving Operational SAR-based Flood Mapping in Arid Regions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6982, https://doi.org/10.5194/egusphere-egu21-6982, 2021.
There is no doubt that the devastating socio-economic impacts associated with floods has been increasing. According to the International Disaster Database (EM-DAT), floods represent the most frequent and most impacting, in terms of the number of people affected, among the weather-related disasters: nearly 1 billion people were affected by inundations in the last decade (2006–2015), while the overall economic damage is estimated to be more than $300 billion. Despite this evidence, and the awareness of the environmental role of rivers and their inundation, our capability to respond to and forecast floods remains relatively poor.
In this context, satellite sensors represent a highly valuable source of observation data that could fill many of the gaps, especially in remote areas and developing countries. In the last decade, with the proliferation of more satellite data and the advent of ESA’s operational Sentinel missions under the EC Copernicus open data programme, satellite images, in particular SAR, have been assisting flood disaster mitigation, response and recovery operations globally.
Although the number of state-of-the-art and innovative research studies in those areas is increasing, the full potential of remotely sensed data to enhance flood mapping has yet to be unlocked, especially the latency issue is not being sufficiently well addressed. Latency, i.e. the time between image acquisition to the flood map delivery to the person that actually needs it, is not at all in line with disaster response requirements and is, to a large extent, responsible for the slow uptake of EO-based products, such as flood maps, into an operational timeline or disaster response protocols of various potential user organizations, such as the UN World Food Programme for instance.
We call to develop a prototype or concept of a product. Specifically, a digital twin experiment should be developed first to generate a prototype AI-based algorithm that could be deployed onboard a SAR satellite to produce flood maps in real time. The mapping result, which consists of simple column/row (x/y) vector indices of flood edges in the form of a short “text message”, will be delivered to the field response teams via satellite communication technology for use within minutes, rather than many hours to days as is currently the case.
In this paper, we illustrate the concept of the proposed innovation, including the future possibility of in-orbit processing. This is in part a synthetic, proof-of-concept study, and, although the societal impact and value of the service prototype developed is clear, once successfully demonstrated, the economic value of this service as well as its market share and value can be established. At this point in time, there is no service of this type in existence. For optical/hyperspectral sensors on CubeSats, onboard AI-based processing has been trialed but not for SAR and not including a rapid flood map delivery service using AI. For instance, based on the results of an ESA-supported FDL Europe challenge, Mateo-Garcia et al. (2019) demonstrated the application of a fully convolutional neural network to prototype a GPU-based onboard flood segmentation system using degraded Sentinel-2 imagery (to mimic CubeSat capability).
How to cite: Schumann, G. J.-P., Giustarini, L., Zare, M., and Gaffinet, B.: Call to action: Pushing scientific and technological innovation to develop an efficient AI flood mapper for operational SAR satellites, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5943, https://doi.org/10.5194/egusphere-egu21-5943, 2021.
Data assimilation uses observation for updating model variables and improving model output accuracy. In this study, flood extent information derived from Earth Observation data (namely Synthetic Aperture Radar images) are assimilated into a loosely coupled flood inundation forecasting system via a Particle Filter (PF). A previous study based on a synthetic experiment has shown the validity and efficiency of a recently developed PF-based assimilation framework allowing to effectively integrate remote sensing-derived probabilistic flood inundation maps into a coupled hydrologic-hydraulic model. One of the main limitations of this recent framework based on sequential importance sampling is the sample degeneracy and impoverishment, as particles loose diversity and only few of them keep a substantial importance weight in the posterior distribution. In order to circumvent this limitation, a new methodology is adopted and evaluated: a tempered particle filter. The main idea is to update a set of state variables, namely through a smooth transition (iterative and adaptative process). To do so, the likelihood is factorized using small tempering factors. Each iteration includes subsequent resampling and mutation steps using a Monte Carlo Metropolis Hasting algorithm. The mutation step is required to regain diversity between the particles after the resampling. The new methodology is tested using synthetic twin experiments and the results are compared to the one obtained with the previous approach. The new proposed method enables to substantially improve the predictions of streamflow and water levels within the hydraulic domain at the assimilation time step. Moreover, the preliminary results show that these improvements are longer lasting. The proposed tempered particle filter also helps in keeping more diversity within the ensemble.
How to cite: Di Mauro, C., Hostache, R., Matgen, P., van Leeuwen, P. J., Nichols, N., and Blöschl, G.: Assimilation of inundation extent observations into a flood forecasting system: a tempered particle filter for combatting degeneracy and sample impoverishment., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10878, https://doi.org/10.5194/egusphere-egu21-10878, 2021.
Rapidly expanding cities are exposed to higher damage potential from floods, necessitating effective proactive management using technological developments in remote sensing observations and hydrological modelling. In this study we tested whether high resolution topographic data derived by Light and Detection Ranging (LiDAR) and Unmanned Aerial Vehicle (UAV) systems can facilitate rapid and precise identification of high-risk urban areas, at the local scale. Three flood prone areas located within the Greater Accra Metropolitan Area in Ghana were surveyed by a UAV-LiDAR system. In order to simulate a realistic flow of precipitation runoff on terrains, Digital Terrain Models (DTM) including buildings and urban features that may have a substantial effect on water flow pathways (DTMb) were generated from the UAV-LiDAR datasets. The resulting DTMbs, which had a spatial resolution of 0.3 m supplemented a satellite-based DTM of 10 m resolution covering the full catchment area of Accra, and applied to a hydrologic screening model (Arc-Malstrøm) to compare the flood simulations. The precision of the location, extent and capacity of landscape sinks were substantially improved when the DTMbs were utilized for mapping the flood propagation. The semi-low resolution DTM projected unrealistically shallower sinks, with larger extents but smaller capacities that consequently led to an overestimation of the runoff volume by 15% for a sloping site, and up to 65 % for 1st order sinks in flat terrains. The observed differences were attributed to the potential of high resolution DTMbs to detect urban manmade features like archways, boundary walls and bridges which were found to be critical in predictions of runoff’s courses, but could not be captured by the coarser DTM. Discrepancies in the derived water volumes using the satellite-based DTM vs. the UAV-LiDAR DTMbs were also traced to dynamic alterations in the geometry of streams and rivers, due to construction activities occurring in the interval between the aerial campaign and the date of acquisition of the commercially available DTM. Precise identification of urban flood prone areas can be enhanced using UAV-LiDAR systems, facilitating the design of comprehensive early flood-control measures, especially in urban settlements exposed to the adverse effects of perennial flooding. This research is funded by a grant awarded by the Danish Ministry of Foreign Affairs (Danida).
How to cite: Trepekli, K., Friborg, T., Balstrøm, T., Fog, B., Allotey, A., Kofie, R. Y., and Møller-Jensen, L.: UAV-LiDAR observations increase the precision of urban flood modelling in Accra by detecting critical micro-topographic features, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10457, https://doi.org/10.5194/egusphere-egu21-10457, 2021.
Extensive areas throughout Europe are affected by river flooding. The frequency of these floods has considerably augmented in the past decades, resulting in substantial economic damage. In the strongly urbanized Flanders region of Belgium, insured losses due to floods are estimated at €40-75 million per year. So far little attention has been paid to off-site source areas of which hydrological behaviour influences the flood risk downstream in the catchment. These off-site areas have however the ability to either increase or reduce the exposure of downstream properties and infrastructures to floods. In rural European landscapes, these off-site areas are characterized by a variety of landscape elements (LSEs) such as hedgerows, trees, drainage ditches and terrace slopes. They affect river discharge and the frequency, extent, depth and duration of floods downstream by creating hydrological discontinuities and connections across the landscape but the magnitude of these effects is very much landscape specific.
We propose a hierarchical workflow to extract vegetated LSEs from LiDAR point data consisting of six steps: (1) selection of non-ground LiDAR points from an airborne LiDAR dataset with an average point density of at least 16 points per square meter, (2) extraction of geometry and eigenvalue based features for each point in the LiDAR point clouds, (3) supervised classification of the points into the classes ‘vegetated LSE’ and ‘other non-ground LiDAR points’ using a Random Forest classifier, (4) clustering of the classified vegetated LSE points by using the density-based clustering algorithm DBSCAN, (5) segmentation of the clustered points by calculating the concave hull per cluster, and (6) classification of the 2D objects into the vegetated LSE classes ‘tree objects’ (individual trees, tree groups and tree rows) and ‘shrub objects’ (bushes, hedgerows and woody edges) by using a Random Forest Classifier and a rule-based approach.
Our workflow was calibrated and tested on two undulating study areas in which the position and geometric characteristics of all vegetated LSEs were recorded in the summer of 2019 using a real-time kinematic GNSS device. The land use in both study areas is dominated by agricultural land. Step 3 of our workflow was validated by using a stratified ten-fold cross-validation method and resulted in a producer’s accuracy of 99% in distinguishing between vegetated LSE and other non-ground LiDAR point. Step 6 resulted in producer’s accuracies between 42% and 64% when distinguishing tree and shrub objects.
Further fine-tuning of the workflow by incorporating features based on point density distributions within LSE segments is expected to increase the classification accuracy. Our aim is to incorporate the classified 2D objects in spatially explicit hydrological models which will allow estimating their effect on river discharge and the frequency, extent, depth and duration of floods downstream.
How to cite: Rosier, I., Diels, J., Somers, B., and Van Orshoven, J.: A workflow to extract vegetated landscape elements from LiDAR point data to study their impact on surface runoff and downstream floods, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4584, https://doi.org/10.5194/egusphere-egu21-4584, 2021.
The study of the riverscape dynamic in lowland areas is crucial for reconstructing the morphoevolution of the drainage network, especially where human activities have always been strongly connected to the river system. Not surprisingly, the Lower Mesopotamian Plain (LMP) represents the ideal study area, being a large floodplain where the Tigris and Euphrates rivers with their distributaries deposited a large volume of sediments during the Holocene. Here, a complex drainage pattern, characterized by paleochannels, levees and crevasse splays developed, representing the expression of several fluvial avulsion processes during the time. Indeed, the presence of recent and ancient crevasse splays in a given area suggests frequent seasonal floods, but at the same time, their formation and growth represent, in the LMP, an important process that conditioned the location of several human settlements since the 6th millennium BC. In this area, about 200 examples of active and abandoned crevasse splays, with various sizes, have been recognized exclusively through a remote sensing approach. The scarce elevation ranges of the LMP represent the main challenge in the detection and mapping of the crevasse splays features (i.e., channels, levees and deposits), in addition to the definition of the floodplain extension and the anthropic impact on channel networks.
Therefore, the research aims to integrate multi-sensor remote sensing data such as optical multispectral imagery and digital elevation datasets for improving the detection and mapping of crevasse splays. Landsat 8 imagery is adopted for computing two spectral indices (NDVI and Clay Ratio) and carrying on different supervised classification methods (i.e., Mahalanobis, Maximum Likelihood, Minimum Distance and SAM). Each method has been evaluated through the computation of the confusion matrix, assessing the Overall Accuracy, K coefficient, Producer Accuracy and User Accuracy. Elevation data used in the topographic analysis to determine the local micro-relief geometry are derived from two different global DEMs available at the ground resolution of 1 arcsec (AW3D30 and GDEM2). Topographic analysis has been performed to complete and validate the supervised classification results.
The outputs successfully demonstrate the potential of the integration of multispectral imagery analysis and topographic analysis from DEM for detecting and mapping with a satisfactory detail the avulsion processes and for distinguishing their state of activity. The methodological approach is a promising technique for flood hazard and risk mapping, as well as for monitoring flood dynamics, especially within arid and semi-arid zones where flawless water management is essential for guaranteeing sustainable crops, livestock and avoiding wasting water.
How to cite: Iacobucci, G., Troiani, F., Milli, S., Piacentini, D., Mazzanti, P., Zocchi, M., and Nadali, D.: Remote sensing approach for the fluvial avulsion processes detection and mapping, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2991, https://doi.org/10.5194/egusphere-egu21-2991, 2021.
In recent years Ireland has experienced significant and unprecedented flooding events, such as groundwater floods, that extended up to hundreds of hectares during the winter flood season, lasting for weeks to months, and affecting many rural communities in Ireland. In response to the serious flooding of winter 2015-2016, specifically related to groundwater, Geological Survey Ireland (GSI) initiated a project (GWFlood, 2016-2019), in collaboration with Trinity College Dublin (TCD) and Institute of Technology Carlow (ITC), to investigate the drivers, map and numerically model the extent of groundwater flooding in Ireland. Through this project, the use of remote sensing data, Sentinel-1 satellite imagery from the European Space Agency Copernicus program, was key to overcome the practical limitations of establishing and maintaining a national field-based monitoring network. The main outputs for this project included: 1) a national historic groundwater flood map, 2) a methodology for hydrograph generation using satellite images, and 3) predictive groundwater flood maps for Ireland.
Subsequently GSI started a new project (GWClimate, 2020-2022), in collaboration with ITC, to monitor floods in Ireland using remote sensing data, to enable short-term forecasting groundwater floods at a national scale, and to evaluate the potential that climate change may have on Irish groundwater resources, both in terms of flooding and drought issues. The GWClimate project is enhancing the tools developed by GWFlood in order to deliver: 1) seasonal flood maps for Ireland, 2) near-real time satellite-based hydrographs, 3) groundwater flood forecasting tools, and 4) maps evaluating the impact of climate change in groundwater systems in Ireland. The outputs of this project will contribute to monitor and quantify the impacts of flooding in Ireland at a national scale, improve the national capacity to understand how groundwater resources respond to climatic stresses, and improve the reliability of adaptation planning and predictions in the groundwater sector.
Data and maps from GWClimate and GWFlood projects are available at: 1) https://gwlevel.ie, and 2) https://www.gsi.ie/en-ie/programmes-and-projects/groundwater/activities/groundwater-flooding/gwflood-project-2016-2019/Pages/default.aspx
How to cite: Campanyà i Llovet, J., McCormack, T., Doherty, D., Schuler, P., Kabza, M., Mullarkey, E., and Naughton, O.: Mapping, Monitoring, Forecasting and Assessing the Impact of Climate Change in Groundwater Systems in Ireland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16012, https://doi.org/10.5194/egusphere-egu21-16012, 2021.
Bathymetric data are a key parameter to assess shallow-water hydrodynamic processes. In-situ surveys provide high data quality; however, surveys are expensive and cover a limited spatial extent. To fill this gap, over recent years, the Satellite Derived Bathymetry (SDB) techniques have been developed. The present work aims to elaborate a technique to estimate bathymetric data from satellite images for intertidal zones. The method applied in this work is composed of 6 steps: (1) image querying and pre-processing is done through Google Earth Engine application (API) using Copernicus Sentinel 2A and B, product type 2A. (2) Identification of the intertidal zone for the study area by temporal variability of the Normalized Difference Water Index (NDWI). (3) Recognition of the waterline in each image by the use of an adaptive threshold technique; and assignment of the elevation for each detected waterline based on local observed tide heights. (4) Validation of the estimated bathymetry by comparison with LiDAR measurements. (5) Implementation of a SDB correction: numerical and/or statistical and, (6) assessment of the validity of SDB for hydrodynamic modelling. The SDB technique was applied to 4 different estuaries in New Zealand: Maketu, Ohiwa, Whitianga and Tauranga Harbour showing similar or better estimations in comparison to previous works using optical or synthetic aperture radar (SAR). For Tauranga Harbour, results from the statistical and dynamical corrections showed that the major error source is due to the image optical properties and environmental conditions when the image was acquired (35%). However, the tidal propagation can significantly decrease the SDB accuracy (13%). Finally, the use of the SDB in numerical simulations does not present huge differences in the predicted waterlevels in comparison to the use of survey bathymetry, showing that SDB could be potentially used for coastal flooding simulations.
How to cite: Costa, W., Bryan, K., and Coco, G.: A waterline method to derive intertidal bathymetry from multispectral satellite images and its application to hydrodynamic modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14286, https://doi.org/10.5194/egusphere-egu21-14286, 2021.
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