Displays

Flooding is the most common and costliest global natural disaster, accounting for 43% of all recorded events in the last 20 years and increasing 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 has been widely adopted by the flood community, from researchers in academia to private companies in the insurance and financial services. 

Planet Labs, Inc. currently operates over 140 satellites, comprising of the largest constellation of Earth observation satellites. The PlanetScope dataset consists of broad coverage, always-on imaging of the entire landmass by 120+ Dove satellites at 3.7 meter resolution. Complementary to PlanetScope, the SkySat dataset includes 15 high resolution satellites imaging at .72 meter resolution with the ability to image any location on Earth twice daily via tasking commands. Next-Generation PlanetScope imagery powered by SuperDove will introduce new spectral bands and interoperability positioned for the increased utilization of Planet imagery by the flood community for both existing and new applications.

How to cite: Zajic, B., Roy, S., and Mascaro, J.: Flooding applications enabled by high resolution, high cadence imagery from the Planet constellation of satellites, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6738, https://doi.org/10.5194/egusphere-egu2020-6738, 2020.

Flooding is one of the most severe natural hazards with respect to both economic and human losses. Therefore, it is of prime importance to provide timely and accurate information about these events, both for emergency management and flood risk assessment. Synthetic Aperture Radar (SAR) sensors are particularly suited to provide flood observations given their all weather, day/night sensing capability and the distinctive backscatter characteristics of smooth water surfaces. Over the past years, a considerable number of SAR-based flood mapping approaches has been developed. However, most of these focus on the retrieval of open water surfaces only. Flood mapping in vegetated and urban areas remains challenging due to the more complex backscatter mechanisms occurring in these areas. Yet, accurate delineation of floods in these areas is all the more important given their economical and societal relevance.

This study focuses on the retrieval of flood extent information in complex, vegetated landscapes by means of freely available data. The considered imagery includes a pair of Sentinel-1 images, one acquired before and one acquired during the flood, as well as a cloud free Sentinel-2 image or mosaic acquired under non-flooded but representative vegetative conditions. An object-based change detection approach is used. Grouping pixels into segments prior to further analysis allows the integration of contextual and morphological information as well as the combination of different information sources. Segmentation is achieved by means of the quickshift algorithm, considering both polarization bands of the SAR image pair. Next, object properties with respect to SAR backscatter, surface reflectance and elevation are calculated and objects are grouped using spectral clustering. By including optical imagery, vegetation cover is considered and the flooded vegetation class can be better discriminated. The resulting clusters are then assessed, analysed and classified. Post-processing is done by means of contextual and topographical information. The use of fuzzy logic allows to assign an uncertainty measure to the obtained classification. For full scene processing, a thresholding-based preliminary flood extent is first derived in order to speed up the classification process and correct for class imbalance. The approach is presented based on multiple study cases, amongst which the 2019 floods along the Sava River, Croatia.

How to cite: Landuyt, L., Verhoest, N., and Van Coillie, F.: An object-based approach for flood mapping in vegetated areas based on Sentinel-1 and Sentinel-2 imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17970, https://doi.org/10.5194/egusphere-egu2020-17970, 2020.

Floods and inundations caused by storm surges and prolonged heavy precipitation frequently affect the Gulf Coast of the United States. During the Atlantic hurricane season, many of the streams and bayous in this region may overflow or break their banks, resulting in severe damage to private properties and public facilities. Reliable information on the spatial distribution and temporal variation of flood and inundation extent is fundamental to the design and implementation of effective disaster preparedness, response, recovery, and mitigation activities. This research aims to develop new algorithms for improved characterization of flood and inundation dynamics using airborne repeat-pass SAR data acquired by NASA/JPL’s polarimetric L-band UAVSAR system. A series of UAVSAR data collected over southeast Texas and southwest Louisiana in summer 2019 are processed to extract surface water extent before and after Tropical Storm Imelda, the fifth-wettest tropical cyclone on record in the continental United States that brought heavy rain and catastrophic flooding. Various metrics derived from polarization decomposition of the quad-polarized radar signals constitute the feature space. Deep learning (DL), a powerful state-of-the-art technique for image classification and big data analytics, is applied and multi-level DL frameworks are established to separate water and partial inundated from land areas. Results show that using fine-tuned 2-D convolutional neural networks (CNNs) with convolutions in both polarimetric and spatial domains can lead to improved classification accuracies over those achieved by conventional machine learning algorithms such as support vector machines (SVMs). Inundation changes with respect to different land-cover/land-use (LCLU) types are also analyzed, and more extensive inundated areas are observed in emergent and non-vegetated wetlands close to the coast. The approaches developed in this study have the potential to assist in future flood and inundation monitoring and impact analysis, and the classified maps created will largely facilitate the investigation of local hydrological processes and water storage assessment.

How to cite: Jin, H. and Liu, T.: Monitoring of Flood and Inundation Dynamics in Coastal Texas and Louisiana Using Airborne UAVSAR Data and Deep Learning Classification Techniques, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12329, https://doi.org/10.5194/egusphere-egu2020-12329, 2020.

Deserts are characterized by high spatial and temporal variability of precipitation resulting in high spatio-temporal variation of floods occurrences. Adjacent drainage basins or even adjacent channels within a single basin may differ significantly and unpredictably in the number of floods per year. Moreover, common river planforms, such as alluvial fans and braided channels are often subjected to high rates of sediment transport and morphological changes, leading to a frequent shifting of the flow. These arise the need for high spatial and temporal resolution mapping of the dynamics of flash floods occurrence and distribution in the active channels. Because of the short duration (few hours to one day) of flash-floods that characterize arid ephemeral streams (wadis), a post- flood index must be applied.

Based on ground monitoring of floods during two hydrological seasons 2017-2018 and 2018-2019 in the Arava vally, Israel , we marked the dates of all flood events and downloaded pre and post satellite images sentinel-2 (A and B) and of Landsat-8) for each event. The combined temporal resolution of both satellites in this area varies from one to five days. We used spectral indices that were originally developed for mapping open water bodies (NDWI, MNDWI) or for monitoring vegetation vigor (NDVI, LSWI). In order to eliminate the varying lithology background effect, we used a normalized time difference equation.

The results show that all bands and indices are sensitive to the flow events. Using single bands change detection is subjected to noise, causing from changes in reflectance that are not due to flood impact. By using the LSWI and MNDWI, this noise considerably eliminates. The results indicated high signal of flood extent when using the MNDWI and LSWI indices even three days after the flood.

This type of monitoring is essential for infrastructure planning, drainage management and river rehabilitation as well as ecological interface. It is also the base for validating models predicting flash floods which save humane lives and properties.

How to cite: Isaacson, S., Rottman, S. R., and Storz-Peretz, Y.: Extracting flash floods distribution and frequencies in arid regions using post flood spectral indices , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17852, https://doi.org/10.5194/egusphere-egu2020-17852, 2020.

In recent decades, remote sensing has widely been used in mapping floods inundations, and many studies have explored the association between antecedent soil moisture and precipitation to assess or predict floods with quantity and intensity. However, capturing the specific flooding events is not always guaranteed because of the satellite poor revisit frequency. Moreover, little attention has been paid to retrieve historic flood inundation based on soil moisture dynamics, especially in the areas with the data scarcity both in terms of soil moisture observations and fine temporal resolution satellite data.

In this study we attempt to explore this issue in two contrasting areas: one arid and one humid, which are the Nile Delta and the Mississippi River Delta, respectively. Several flooding events are selected to conduct specific flood inundation analysis. Multiple satellite microwave soil moisture products are analysed, including European Space Agency Climate Change Initiative (ESA-CCI) Soil Moisture, Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer (AMSR-2) and ESA Sentinel satellite imagery.

Considering that the soil moisture decreases more slowly than the surface flooded water, the present study aims to retrieve historic flood inundation based on soil moisture dynamics from satellites, and the main objectives are: (1) to make a comparison on spatial and temporal dynamic patterns of the above-mentioned products in two study areas; (2) to investigate a method for distinguishing the flooded areas and the areas which are always fully saturated; (3) to develop an approach for detecting historic flood inundation based on soil moisture dynamics; and (4) to calibrate the soil moisture output from WRF-Hydro model using satellite soil moisture observations. Results are expected to be applicable for decision-making in flood disaster relief and flood prediction.

How to cite: Wang, J., Chen, O., Chen, Y., Liu, Y., Zhuo, L., Rico-Ramirez, M., and Han, D.: Flood inundation mapping with multi-satellite soil moisture observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9225, https://doi.org/10.5194/egusphere-egu2020-9225, 2020.

The main objective of this study is to investigate how innovative satellite earth observation techniques that allow for the estimation of soil moisture and the mapping of flood extents can help in reducing errors and uncertainties in conceptual hydro-meteorological modelling especially in ungauged areas where potentially no or limited runoff records are available. A spatially distributed conceptual hydrological model is first developed allowing for the simulation of soil moisture and flood extent. Using as forcing of this model rainfall and air temperature time series provided in the globally and freely available ERA5 database it is then possible to carry out long-term simulations of soil moisture, discharge and flood extent. Next, time series of soil moisture and flood extent observations derived from freely available satellite image databases are jointly assimilated into the hydrological model in order to retrieve optimal parameter sets. For this assimilation experiment, we take benefit of recently introduced Particle Filters with tempering that circumvent some of the usual particle filter limitations such as degeneracy and sample impoverishment. As a proof of concept, we set up an identical twin experiment based on synthetically generated observations and we evaluate the performance of the calibrated model.

How to cite: Hostache, R., Matgen, P., van Leuuwen, P.-J., Nichols, N., Chini, M., Pelich, R., and Delenne, C.: Assimilating satellite soil moisture and flood extent maps into a flood prediction model., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13848, https://doi.org/10.5194/egusphere-egu2020-13848, 2020.

Karst related groundwater flooding represents a significant hazard in many rural communities in Ireland. A series of unprecedented flood events in recent years have reinforced the need to improve our ability to quantify the location and likelihood of flood occurrence. Geological Survey Ireland, in collaboration with Carlow Institute of Technology and Trinity College Dublin, has established a collaborative project to investigate groundwater flooding, with particular emphasis on seasonal karst lakes known as turloughs. There are over 400 recorded turloughs across Ireland, the majority of which located on limestone lowlands. Turloughs can completely dry during summer months but extend to hundreds of hectares during the winter flood season. The practical limitations of establishing and maintaining a network of over 400 turloughs supported the use of remote sensing and GIS techniques to delineate flood extents and monitor flood prone areas using satellite imagery such as of the ESA Copernicus programme. Measurements at 50 sites for over 18 months were used to calibrate and validate results from satellite data. With limited recorded groundwater flood data in Ireland, the use of remote sensing data provides historical archives of images to look at past flood conditions to optimise the detection of groundwater and delineate maximum groundwater flood maps. These new data improve the fundamental hydrological understanding of groundwater flooding in Ireland, enabling key stakeholders to develop appropriate flood mitigation measures and allow for informed flood assessments to be made in future. Additionally, it is a first step towards implementation of near-real time monitoring and forecasting of groundwater levels, and the evaluation of the impact of climate change to groundwater systems in Ireland.

How to cite: Campanyà i Llovet, J., McCormack, T., and Naughton, O.: Remote Sensing for Monitoring and Mapping Karst Groundwater Flooding in the Republic of Ireland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18921, https://doi.org/10.5194/egusphere-egu2020-18921, 2020.

Floods can have extremely expensive and often fatal consequences, thereby necessitating accurate flood inundation forecasts for increased preparedness and resilience. In an operational forecasting scenario, inflow uncertainties propagated from precipitation forecasts dominate and lead to inherently erroneous predictions of flood inundation, frequently impeding their application to rescue and response operations. Thus, it is expected that assimilating independent inundation observations, from Synthetic Aperture Radar (SAR) sensors for example, may reduce the inherent uncertainty in hydraulic modelling. The increasing number of SAR satellites, with their all-weather/all-day imaging capabilities, have increased the probability of monitoring flood dynamics from space. SAR-based flood extents were previously used to indirectly retrieve floodplain water levels in conjunction with digital elevation models. However, studies highlighted this process as an additional source of uncertainty, leading to the development of algorithms for the direct assimilation of flood extent into hydraulic flood inundation forecasting chains. The efficiency of flood extent assimilation is keenly sensitive to the spatiotemporal observation characteristics, and so the expected improvement in the forecast strongly depends on the acquisition timing with respect to the position of the flood wave. In this study, numerical experiments were used to simulate multiple spatiotemporal SAR acquisition scenarios, to identify the optimum measurement design for targeted satellite acquisition, to best facilitate flood extent assimilation. A particle filter based flood extent assimilation framework was developed using the hydraulic model LISFLOOD-FP, and implemented for the 2011 flood event in the Clarence Catchment, Australia. An operational forecasting scenario was emulated for the open loop model ensemble, with the consideration of temporally correlated, variance changing uncertainties in inflows, simulating hydrological model forecasts. The impact of assimilating flood extent at reaches exhibiting uniform flow behaviour, with different combinations of first visit and revisit intervals were investigated. Results indicate that the optimum timing and frequency of targeted SAR acquisitions differs with respect to reach hydraulic characteristics and that images acquired after the peak is observed in the channel are most informative for the forecast. Note that the maximum inundation extent in the floodplain always follows the channel peak, and therefore, post-peak images with respect to the within reach flood wave could improve predictions during the peak in the floodplain. Moreover, a single image assimilated at a reaches exhibiting more diffusive flow behaviour just after the peak, could result in improvements comparable to the assimilation of multiple images elsewhere. Findings from the study will allow the optimal utilization of SAR imagery to overcome localized model uncertainties, and help to maximize the accuracy of inundation forecasts.

• Keywords: Flood inundation modelling, flood extent assimilation, SAR, data assimilation, hydraulic modelling, forecast uncertainty

How to cite: Dasgupta, A., Hostache, R., Ramsankaran, R., Schumann, G., Grimaldi, S., Pauwels, V., and Walker, J.: Optimizing SAR-based Flood Extent Assimilation for Improved Flood Inundation Forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4342, https://doi.org/10.5194/egusphere-egu2020-4342, 2020.

Floods and their impacts are highly local in nature and vulnerable population and exposed assets are most at risk in coastal monsoonal regions. This is aggravated if the region is also exposed to tropical cyclones, such as Mozambique and the Licungo basin along the eastern coastline of the country.

In order to be better prepared against future high-impact flood events, Mozambique’s National Institute for Disaster Management (INGC) has mapped the watershed of the country’s central Licungo River with drones to reduce flood risks and improve emergency response planning. The mapping is intended to “minimize risks” and promote timely preparation of actions when cyclones and floods are expected in the area.

In the proposed project, the acquired drone terrain model and collected field data (water levels) will be used to drive a bespoke localized 2-D flood model to accurately reproduce flood hazard and risk in the central Licungo basin for the 2013 and 2015 flood disasters. In addition, high-resolution population and exposure layers have been used to define bespoke local flood risk maps.

Accurate flood risk assessment of past events at the local scale can better inform decision support systems and facilitate the decision-making process and preparedness for future high-impact events. Knowing who is at risk where and when is vital information that is missing in many vulnerable regions and is most of the time not available at the required local level.

Moreover, global or large-scale flood prediction models do not contain the necessary detail to infer meaningful flood risk at the local level and such models are known to be inaccurate, albeit they represent best efforts at the scales they are simulating. However, to what degree these models are wrong at the local scale of impact and what is needed to improve them is not known, largely because local flood data and bespoke predictions of flood risk are missing at the local scale for many vulnerable regions. The collected high-resolution data and the local flood risk assessment this project proposes would allow the validation of large-scale modeling efforts thereby advancing our understanding of model limitations and would create opportunities to improve them at large scales.

How to cite: Manalili, M. A., Schumann, G., Prades, L., Rosa, S., Reane, D., and Beleza, A. J.: The Value of Drones for Bespoke Local Flood Risk Assessment in the Licungo Basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4473, https://doi.org/10.5194/egusphere-egu2020-4473, 2020.

Drought and flood occur frequently in the Pearl River Basin (PRB), leading to severe damage and economic losses. For better basin-scale water resources management, this study investigates drought and flood  and connection to climate variability in PRB using the total terrestrial water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) satellites. Water storage deficit (WSD) and WSD index (WSDI) are used to characterize drought in PRB. A total of nine drought events are identified during the study period 2003-2014. The period 2003-2006 experienced the most serious drought with a duration of 34 months and WSD (or total severity) of over 1200 mm. WSDI is comparable to self-calibarated Palmer Drought Severity Index (scPDSI) in timing with a correlation of 0.80. Overall, WSDI has higher magnitude than the scPDSI throughout the study period. Flood is characterized by a flood potential index (FPI) which is calculated using TWS anomaly and precipitation. The FPI peaked in June 2008 when the flood was the most serious with the largest rainfall and discharge. Strong correlation is found between FPI and rainfall/discharge in all the four seasons indicating the joint control of flood by rainfall and discharge. This study analyzes the relationship between drought, flood and four climate indices (i.e. El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD) and North Atlantic Oscillation (NAO)). Different drought events were related to different climate indices. The severe drought during 2003-2005 was triggered by a warm PDO phase. The 2009-2010 drought was jointly influenced by the warm phase of the three indices: ENSO (i.e. El Niño), IOD and PDO. The severe drought in 2011 was related to the cool phase of both PDO and ENSO (i.e. La Nina). The flood in 2008 was mainly induced by the cool PDO phase with the combined effect from IOD and NAO.

How to cite: Huang, Z., Jiao, J. J., Luo, X., and Pan, Y.: Drought and flood monitoring and connection to climate variability in Pearl River Basin, Southern China using GRACE data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4600, https://doi.org/10.5194/egusphere-egu2020-4600, 2020.

Describing spatio-temporal dynamics of a flood using an object-based approach with a graph-based representation proved useful for analysis of small-scale flood dynamics in Belgium (about 100 km²) (Debusscher, et al., 2019).  Starting from pre-processed Sentinel-1 SAR imagery, the water bodies are delineated in each timestep (using a thresholding algorithm), after which all water-polygons are grouped into graphs according to their spatial overlap on consecutive timesteps.  Change in (water)area and backscatter are used to quantify the amount of variation.  Products of this tool are a global variation map covering the whole study are, and a temporal profile for each waterbody, visually describing the evolution of the backscatter and number of polygons that make up the waterbody.  
After establishing this proof of concept in a small region (flood of June 2016 in Schulensbroek, a nature reserve in north-eastern Belgium), this approach is applied on floods covering larger areas (about 10000 km²).  Two cases are studied, the Mozambique flood of March 2019 (near Beira) and the India flood of September 2019 (near Patna).  The process of upscaling leads to solving issues regarding the minimal mapping unit, adding extra pre-processing in order to simplify polygons (morphological operators), increasing code efficiency (mainly regarding for-loops).
In the absence of ground truth, produced flood maps are compared to existing flood extent maps (from Disaster Charter (unitary) and Hasard (LIST)) in order to estimate accuracy.

References
Debusscher Bos and Van Coillie Frieke Object-Based Flood Analysis Using a Graph-Based Representation, Remote Sensing. - 2019. - p. p. 1883.

How to cite: Debusscher, B. and Van Coillie, F.: Object-Based Flood Analysis Using a Graph-Based Representation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4922, https://doi.org/10.5194/egusphere-egu2020-4922, 2020.

The Surface Water and Ocean Topography (SWOT) satellite mission will provide high-resolution estimates of riverine water surface characteristics, such as river surface width, elevation and slope. Those parameters will enable a global estimation of river discharges flowing into rivers wider than 100 m, with a temporal resolution varying from 3 to 10 days, in dependence of latitude. Although errors on streamflow estimates are expected to be highly dependent on flow regimes and geomorphic conditions, the mission potential on providing insights on the hydrological regime of inland rivers is still not fully investigated. To this end, in this study we propose a comparison of remotely sensed and empirical period-of-record flow-duration curves (FDCs) on worldwide basis. We used the Global Runoff Data Centre (GRDC) dataset, the world largest and freely available source of streamflow data. We filtered the original dataset by selecting only those sites that matched 2 criteria: river width larger than 100 m and streamflow time series longer than 10 years of continuous daily discharges. Such dataset query resulted in 1200 gauged river cross-sections readily available to be used for our purposes. To simulate SWOT observations, each record has been reduced following 4 different sampling scenarios, i.e. 3, 5, 7, and 10 days interval for a 3-year moving time-frame (i.e., SWOT mission lifetime). We then corrupted gauged data with random errors sampled from a gaussian distribution having zero mean and 30% standard deviation. For each site, we obtained a set of SWOT simulated FDCs to compare with their empirical counterparts. We found that tropical and temperate climates deliver good estimates throughout flow regimes, whereas, mostly arid climates may have higher uncertainties, especially for high- and low-flows.

How to cite: Domeneghetti, A., Pugliese, A., Castellarin, A., and Brath, A.: SWOT Mission Capabilities for the Prediction of Flow-Duration Curves: A Global Scale Assessment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7359, https://doi.org/10.5194/egusphere-egu2020-7359, 2020.

Pluvial (surface water) flooding is often the cause of significant flood damage in urban areas. However, pluvial flooding is often overlooked in catchments which are historically known for fluvial floods. In this study, we present a conceptual remote sensing-based integrated approach to enhance current practice in the estimation of flood extent and damage and characterise the spatial distribution of pluvial and fluvial flooding. Cockermouth, a small town in England which is highly prone to flooding, was selected as a study site and the flood event caused by storm Desmond in 2015 (5-6/12/2015) was selected for this study. A high-resolution digital elevation model (DEM) was produced from a composite digital surface model (DSM) and a digital terrain model (DTM) obtained from the Environment Agency. Using this DEM, a 2D flood model was developed in HEC-RAS (v5) 2D for the study site. Simulations were carried out with and without pluvial flooding. Calibrated models were then used to compare the fluvial and combined (pluvial and fluvial) flood damage areas for different land-use types. The number of residential properties affected by fluvial and combined flooding was compared using a combination of modelled results and data collected from Unmanned Areal System (UAS). As far as the authors are aware, this is the first time remote sensing data, hydrological modelling and flood damage data at property level have been combined to differentiate between the flood extents and damage caused by fluvial and pluvial flooding in the same event. Results show that the contribution of pluvial flooding should not be ignored even in a catchment where fluvial flooding is the major cause of the flood damages. Although the additional flood depths caused by the pluvial contribution were lower than the fluvial flood depths, the affected area is still significant. Pluvial flooding increased the overall number of affected properties by 25%. In addition, it increased the flood depths in a number of properties that were identified as being affected by fluvial flooding, in some cases by more than 50%. These findings show the importance of taking pluvial flooding into consideration in flood management practices. Further, most of the data used in this study were obtained via remote sensing methods, including UASs. This demonstrates the merit of developing a remote sensing-based framework to enhance current practice in the estimation of flood extent and damage.

How to cite: Muthusamy, M., Rivas Casado, M., Salmoral, G., Irvine, T., and Leinster, P.: A remote sensing based integrated approach to quantify the impact of fluvial and pluvial flooding in an urban catchment , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9313, https://doi.org/10.5194/egusphere-egu2020-9313, 2020.

Two-dimensional (2-D) hydraulic models are used for the prediction of floodplain inundation spatio-temporal patterns to improve flood risk estimation, and support emergency and land management. Accurate model calibration is pivotal to enable adequate representation of flood dynamics and requires the comparison between model predictions and observed data.

Remote sensing (RS) observations of inundation extent and water level allow model evaluation at a large number of locations in the floodplain, providing opportunities for a thorough understanding of inundation dynamics. However, RS instruments provide information at a snapshot in time and so the existing performance metrics generally compare model results and observations at the acquisition time. Nevertheless, explicitly differentiating between model parameterizations which underpredict or overpredict the flood wave arrival time is valuable to assess models’ predictive skill.

In 2-D hydraulic models, roughness values are considered to be the most important parameters controlling the flow characteristics and so they are used for model calibration. Although RS-derived spatially distributed information allows the tuning of a large number of spatially distributed roughness values, the calibration framework must enable parameter identifiability while avoiding overfitting and equifinality problems. Another challenge affecting the calibration exercise is the computational burden of 2D-hydraulic models, which generally hampers the application of frameworks requiring a large number of model realizations.

This presentation introduces a novel framework for the calibration of 2D hydraulic models. Specifically, the calibration framework was designed to (1) make exclusive use of RS-derived observations and consequently enable model calibration in ungauged catchments; (2) allow discriminating between underprediction and overprediction of flood wave arrival time; (3) identify a parameter configuration which is robust for different flood events; and (4) require a limited number of model realizations.

A novel performance metric, the Space-Time-Score, is therefore proposed to compare modelled and observed water level and discriminate between underestimation and overestimation of flood wave arrival time, with binary performance metrics used to compare modelled and observed inundation extents. These performance metrics allow quantifying the capability of different parameter sets to reproduce the observed data.  A novel set of river roughness values is then computed to minimise the discrepancy between model results and observations.

The 2011 and the 2013 flood events in the Clarence catchment (Australia) were used as test cases. The 2D hydraulic model was LISFLOOD-FP; available remote sensing data included both Synthetic Aperture Radar and optical acquisitions. Gauged data were used as an independent validation dataset and demonstrated the effectiveness of the proposed framework to identify a spatially distributed parameter set which is robust for different flood events.   

Despite the promising results of this initial testing, it is imperative to underline that the proposed framework was designed to minimise the discrepancies between model results and observations. Consequently, RS accuracy, timing and spatial coverage are expected to affect the performance of the calibration. For this reason, extensive further testing is essential to investigate the impacts of RS features on the effectiveness of the proposed methodology for a number of catchments with different morphologies and flooding dynamics.

How to cite: Grimaldi, S., Wright, A. J., Walker, J. P., and Pauwles, V. R. N.: Verification of flood wave arrival time predictions using remote sensing-derived water levels, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11617, https://doi.org/10.5194/egusphere-egu2020-11617, 2020.

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. Emergency responders frequently request satellite-based crisis information for flood monitoring to target the often-limited resources and to prioritize response actions throughout a disaster situation. Flood mapping algorithms are usually based on automatic thresholding algorithms for the initialization of the classification process in SAR amplitude data. These thresholding processes like Otsu thresholding, histogram leveling etc., are followed by clustering techniques like K-means, ISODATA for segmentation of water and non-water areas. These methods are capable of extracting the flood extent if there is a significant contrast between water and non-water areas in the SAR data. However, the classification result may be related to overestimations if non-water areas have a similar low backscatter as open water surfaces and also, these backscatter values differentiate from VV and VH polarizations. Our method aims at improving existing satellite-based emergency mapping methods by incorporating systematically acquired Sentinel-1A/B SAR data at high spatial (20m) and temporal (3-5 days) resolution. Our method involves a supervised learning method for flood detection by leveraging SAR intensity and interferometric coherence as well as polarimetry information. It uses multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. By incorporating multitemporal satellite imagery, our method allows for rapid and accurate post-disaster damage assessment and can be used for better coordination of medium- and long-term financial assistance programs for affected areas. In this paper, we present a strategy using machine learning for semantic segmentation of the flood map, which extracts the spatio-temporal information from the SAR images having both intensity as well coherence bands. The flood maps produced by the fusion of intensity and coherence are validated against state-of-the art methods for producing flood maps. 

How to cite: Ghosh, B., Motagh, M., Haghshenas Haghighi, M., and Maghsudi, S.: Automatic Flood Monitoring based on SAR Intensity and Interferometric Coherence using Machine Learning , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12954, https://doi.org/10.5194/egusphere-egu2020-12954, 2020.

Floods are happening regularly in almost all places of the world and impact people, societies and economies, causing widespread devastation that can be hard to recover from. Yet, accurately predicting and alerting for floods is challenging, primarily since flood events are very local in nature and processes causing a flood can be very complex. In an era of open-access geospatial data proliferation as well as data and model interoperability, it makes sense to leverage on existing data and models, many of which are underutilized by decision-making applications. Thus, the objective of the project is to develop an open-access rapid alerting and severity assessment component for global flooding based on existing models and observation data sources. We do this within the DisasterAWARE platform of the Pacific Disaster Center (PDC).

This paper will outline the proposed concept of model-of-models that will leverage existing flood-hazard modeling capabilities, illustrating products that we will leverage, such as: GLOFAS (Global Flood Forecasting Feeds) probabilistic hydrologic data, IMERG (The Integrated Multi-satellitE Retrievals for GPM) observed precipitation grids, GDACS (Global Disaster Alerting Coordination System) anomaly points, GFMS (Global Flood Monitoring System) depth above baseline grids, the NASA MODIS (Moderate Resolution Imaging Spectroradiometer) and Dartmouth Observatory flood maps, as well as new models as they are developed. We will further combine the flood hazard data with existing exposure data to estimate property loss using a probabilistic fragility approach. With the use of an end-to-end deep learning framework, structural damage will be detected using different remote sensing data. The approach will further incorporate other, non-routinely-generated remotely-sensed products for ground-truthing for areas and events where and when such products are available.

The existing resilience and capacity of communities to rapidly respond to and recover from flood impacts will be incorporated into the severity determination on an administrative area and watershed risk basis. This model-of-models approach will leverage major efforts, improve reliability and reduce false triggers by ensuring two or more models agree.

How to cite: Schumann, G. J.-P., Glasscoe, M., Bausch, D., Pierce, M., Wang, J., Chen, Z., Eguchi, R., Huyck, C., Tiampo, K., and Kar, B.: Using a model-of-models approach and remote sensing technologies to improve flood disaster alerting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13113, https://doi.org/10.5194/egusphere-egu2020-13113, 2020.

Change detection has been widely used in many flood-mapping algorithms using pairs of Synthetic Aperture Radar (SAR) intensity images. The rationale is that when the right conditions are met, the appearance of floodwater results in a significant decrease of backscatter.  However, limitations still exist in areas where the SAR backscatter is not sufficiently impacted by surface changes due to floodwater. For example, in shadow areas, the backscatter is stable over time because the SAR signal does not reach the ground due to prominent topography or obstacles on the ground (e.g., buildings). Densely vegetated forest is another insensitive region due to low capability of SAR C-band wavelengths to penetrate its canopy. Moreover, although in principle SAR can sense water over different land cover classes such as arid regions, streets and buildings, the backscatter changes over time could not be detected because in such areas the scattering variation caused by the presence of water might be negligible with respect to the normal “unflooded” state. The identification of the abovementioned areas where SAR does not allow detecting water based on change detection methods, hereafter called exclusion map, is crucial for providing reliable SAR-based flood maps.

In this study, insensitive areas are identified using long time-series of Sentinel-1 data and the final exclusion map is classified in four distinctive classes: shadow, layover, urban areas and dense forest. In the proposed method the identification of insensitive areas is based on the use of pixel-based time series backscatter statistics (minimum, maximum, median and standard deviation) coupled with a local spatial autocorrelation analysis (i.e. Moran’s I, Getis-Ord Gi and Geary’s C). In order to evaluate the extracted exclusion map, which is quite unique, we employ a comprehensive ground truth dataset that is obtained by combining different products: 1) a shadow/layover map generated using a 25m-resolution DEM and the geometric acquisition parameters of the SAR data; 2) 20m resolution imperviousness map provided by Copernicus, as well as a high-resolution global urban footprint (GUF) data provided by DLR; 3) a 20m tree cover density (TCD) map provided by Copernicus. In the end, the exclusion map is used to mask out unclassified areas in the flood maps derived by an automatic change detection method, which is expected to enhance flood maps by removing areas where the presence or absence of floodwater cannot be evidenced. In addition, we argue that our insensitive area map provides valuable information for improving the calibration, validation and regular updating of hydraulic models using SAR derived flood extent maps.

How to cite: Zhao, J., Chini, M., Pelich, R., Matgen, P., Hostache, R., Cao, S., and Wagner, W.: Generating an exclusion map for SAR-based flood extent maps using Sentinel-1 time series analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13743, https://doi.org/10.5194/egusphere-egu2020-13743, 2020.

Long time series of flood extent mapping are valuable for flooding frequency analysis, wetlands monitoring and hydrological model validation. In this study an automatic algorithm for flood extent mapping using long time series of synthetic aperture radar (SAR) imagery and observed water levels or discharge is presented. The key assumption of this algorithm is that the flooding extent is correlated to these two observed variables and the highest correlation is obtained when the flood/no flood threshold value of SAR backscatter coefficient is optimal. This study is conducted in the Biebrza River floodplain (approximately 220km²) located in NE Poland. The floodplain is a natural wetland, relatively untouched by human, with complex inundation that involves not only river flooding, but also groundwater discharge and rain or snowmelt local inundation. In order to map 2014-2018 flooding series the automatic thresholding algorithm is run on Sentinel 1 data from one relative orbit, yielding 161 SAR scenes. The estimated 2014-2018  water line match well water levels from independent water gauge and the inundation maps agree with the MODIS 500m reflectance image. This approach was unable to identify inundation in remote parts of the floodplain except very intensive groundwater discharge events. This behavior may have several reasons, of which the most probable are that the dense vegetation obscuring inundated ground and that groundwater, snowmelt or rainfall inundation is not correlated to the variables recorded at a water gauge located in the river.

How to cite: Berezowski, T.: Automatic flood extent mapping using long time series of SAR imagery and water levels or discharge data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16811, https://doi.org/10.5194/egusphere-egu2020-16811, 2020.

The demand for timely and accurate flood information is well understood and more urgent than ever as flooding has become the most common natural hazard worldwide, impacting people of all continents in both developed and less developed countries. Population and total exposed assets by river flooding are certain to increase in the coming century making the need for flood information even more pressing. Unlike the World Meteorological Organization (WMO), the hydrological community hasn’t been very successful in establishing a global hydrological network of observations through which model simulations and measurements and novel measurement technologies could be exploited. Countries that can afford have departments in place that are tasked to develop flood risk maps and are involved in flood forecasting and relief efforts. However, the majority of countries do not or cannot allocate sufficient funds to support such efforts, nor has there been a global initiative to identify and determine global flood risk areas.

Due to the lack of objective knowledge of the impact of flooding during or after an event, first relief agency assistance is often constrained and therefore less effective. These humanitarian catastrophes could be reduced with better transformation of existing observational and modeling technologies into information useful to local populations and decision makers.

Here I present new efforts to produce a state-of-the-art, globally-scoped, flood prediction, monitoring capabilities and risk evaluations platform that is interactive and includes high resolution flood information to better serve local needs. The platform builds upon already operational or quasi-operational NASA-supported global flood systems, including the DFO - Flood Observatory satellite-based hydrological gauging stations, UMD Global Flood Monitoring System (GFMS) and have these integrated with the European Commission’s GloFAS, and SAR-based high-resolution flood mapping. This with the intension to have these data layers (flood forecasting, flood extent, and flood history) available to everybody.

How to cite: Kettner, A., Schumann, G., Brakenridge, R., Adler, B., Policelli, F., Slayback, D., Matgen, P., Souffront, M., and Shen, X.: Dissemination of modeled and satellite derived flood products: Global coverage to support local needs, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22125, https://doi.org/10.5194/egusphere-egu2020-22125, 2020.

Wetlands are among the most biodiverse ecosystems in the world, due largely to their dynamic hydrology. Frequent observations by satellite sensors such as the Moderate Resolution Imaging Spectrometer (MODIS) allow for monitoring the seasonal, inter-annual and long-term dynamics of surface water extent. However, existing MODIS-based studies have only demonstrated this for large water bodies despite the ecological importance of smaller-sized wetland systems. In this paper, we constructed the temporal dynamics of surface water extent for 340 individual water bodies in the Mediterranean region between 2000 and 2017, using a previously developed 8-day 500 m MODIS surface water fraction (SWF) dataset. These water bodies has a wide range of size, specifically 0.01 km² and larger. We then compared the water extent time series derived from MODIS SWF with those derived from a Landsat-based dataset. Results showed that MODIS- and Landsat-derived water extent time series showed a high correlation (r = 0.81) for more dynamic water bodies. Our MODIS SWF dataset can also effectively monitor the variability of very small water bodies (<1 km²) when comparing with Landsat data as long as the temporal variability in their surface water area was high. We conclude that MODIS SWF is a useful product to help understand hydrological dynamics for both small and larger-sized water bodies, and to monitor their seasonal, intermittent, inter-annual and long-term changes.

How to cite: Li, L., Vrieling, A., Skidmore, A., and Wang, T.: Constructing Mediterranean wetland open water dynamics using a new 18-year MODIS-derived surface water fraction dataset, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19945, https://doi.org/10.5194/egusphere-egu2020-19945, 2020.

The spatial reference framework is the lateral extent of the river channel and its floodplain, named “flood-prone area extent”. Due to human interventions into the hydrological cycle and morphological alterations of the river channel and its floodplain, some areas that were regularly flooded once, may not experience such flooding today. We characterize them as “former floodplains”. Floodplains flooded now are named “active floodplains”. The analysis is done on the spatial resolution level named “Functional Elementary Catchment” (FEC) of the European catchments and Rivers network system (Ecrins) database and for the flood-prone areas in Europe that include former and active floodplains with river channels. It is named “Potential flood prone area”. In the first step we defined floodplains typology. For the assessment part we developed indicators of floodplain forms and processes, defined their benchmark condition and performed quality classification. Here, we describe what spatial data we used and what data we still miss to produce reliable assessment.   

The spatial layer “Potential flood-prone area extent” was derived from two spatial layers, Potential Riparian Zone Delineation of the Copernicus Land Monitoring Service and JRC flood hazard map for Europe 100-year return period, a result of flood model “LisFlood”.  

The candidate list of typology factors included 31 factors derived from various databases such are Ecrins, MARS, FAO, Copernicus, WorldClim, PCGLOBEWB and IHME. Factors represent abiotic state before human intervention into rivers and floodplains and are grouped into regions, climate, morphology, hydrology, geology and  physics – river dynamics. The calculated factors are reasonably covering the assessment area (95% - 99%) with the exception of the physics – river dynamics factors. This information was obtained for less than 30% of European area. The selection of factors defining floodplain types was based on the criterion of adequate spatial coverage, reliability and non-redundancy. As a result, floodplain types were derived from seven factors, three morphological (river average altitude and slope and average floodplain width), one geological (dominant catchment geo-chemistry) and three hydrological factors (specific runoff as mean annual discharge divided by catchment area, high flow duration and high flow pulses). Hydrological and morphological factors are only approximations to the natural state, so we propose to further develop databases providing information on river and floodplain hydromorphology prior to major human interventions.

Indicators of floodplain forms are derived from two layers, Riparian Zone Land Cover/Land Use and High Resolution Water & Wetness of the Copernicus Land Monitoring Service. The land use layer provides a good basis for assessing the current distribution of floodplain habitats. We also estimate the size of the active natural floodplains using wetness data, but the results can be improved with systematic European wide information on present hydrotechnical structures and hydromorphological alterations. Such data would also support assessment of floodplain ecological condition and management options.  

How to cite: Globevnik, L., Birk, S., Januschke, K., Kail, J., Snoj, L., Lyche Solheim, A., Azlak, M., and Christiansen, T.: Assessing naturalness of European floodplain hydromorphology using remote sensing products and other consistent large scale data , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22039, https://doi.org/10.5194/egusphere-egu2020-22039, 2020.