Using COSMO-SkyMed images to improve river flood monitoring and forecasting.

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Using COSMO-SkyMed images to improve river flood monitoring and forecasting. D.C. Mason 1, S.L. Dance 23, J. Garcia-Pintado 24, S. Vetra-Carvalho 2, H.L. Cloke 125, P.D. Bates 6 1 Department of Geography and Environmental Science, University of Reading, Reading, UK.

1 Using COSMO-SkyMed images to improve river flood monitoring and forecasting. D.C. Mason 1, S.L. Dance 23, J. Garcia-Pintado 24, S. Vetra-Carvalho 2, H.L. Cloke 125, P.D. Bates 6 1 Department of Geography and Environmental Science, University of Reading, Reading, UK. 2 Department of Meteorology, University of Reading, Reading, UK. 3 Department of Mathematics and Statistics, University of Reading, Reading, UK 4 Centre for Marine Environmental Studies (MARUM), University of Bremen, Germany. 5 Department of Earth Sciences, Uppsala University, Uppsala, Sweden. 6 School of Geographical Sciences, University of Bristol, Bristol, UK.

2 CORSAIR flooding projects High resolution satellite SAR sensors are now commonly used in natural disasters such as flooding. If the SAR images can be obtained in near real-time, they can be used for operational flood relief management and improved flood forecasting. CSK images were acquired under two CORSAIR projects related to flooding. 1. CORSAIR003: Improved river flood forecasting. 2. CORSAIR024: Delineation of flood water in urban areas.

3 CORSAIR003: Acquisition of COSMO-SkyMed image sequences for improved river flood forecasting. Great deal of spatial information in SAR flood extents. We have developed a prototype system for extracting flood extents from SAR images and using them to improve flood forecasting using data assimilation. Includes algorithm for delineating flood extent in SAR image in near real-time. Water level observations (WLOs) estimated at flood extent boundaries by intersecting them with floodplain DTM. CSK image of flooding in 2012, blue flood extent overlaid. Selected WLOs assimilated into flood inundation model. Assimilation improves model state and estimates model parameters.

Improving flood forecasting using data assimilation Most useful images acquired on rising limb of hydrograph. Object of study was to acquire complete sequences of floods, to improve prototype system.

4 Improving flood forecasting using data assimilation Most useful images acquired on rising limb of hydrograph. Object of study was to acquire complete sequences of floods, to improve prototype system. Study area = lower Severn and Avon rivers around Tewkesbury, UK. Daily COSMO-SkyMed images of 2012 flood, with flood extents (blue ) overlain (COSMO-SkyMed Product - ASI [2012] processed under license from ASI Agenzia Spaziale Italiana. All rights reserved. Distributed by e-geos).

5 Trigger Mechanism CSK constellation useful for flood monitoring because it can provide very high (3m) resolution images of a flood at 12-hour revisit intervals, giving a high temporal sampling frequency. Emergency Programming mode employed. Problem was there is a delay of about hours between a tasking request being made by the user and the first image in a sequence being acquired. Our primary trigger mechanism was the Flood Guidance Statement (FGS) from FFC. The FGS is based on broad scale weather information, so if it says there is a medium/high risk of river flooding, we also needed to obtain flood warnings from the EA Midlands regional flood forecasting centre based on detailed local forecasting models and actual river levels. Images gathered for mini-flood of 22-25/11/ CSK and also 1 Sentinel-1 pre-flood image acquired.

6 Sentinel-1 22/11/2016 (morning) (dark areas are water)

CSK 23/11/2016 (evening) (COSMO-SkyMed Product - ASI [2016] processed under

CSK 24/11/2016 (morning) (COSMO-SkyMed Product - ASI [2016] processed under

CSK 24/11/2016 (evening) (COSMO-SkyMed Product - ASI [2016] processed under

CSK 25/11/2016 (evening) (COSMO-SkyMed Product - ASI [2016] processed under

11 CORSAIR003: Conclusions Unfortunately there was no out-of-bank image clearly on the rising edge. The triggering mechanism seems to have worked quite but not very well, because it was a day late being activated. Unfortunately for this flood no alert was obtained from upstream at Shrewsbury, which would have given an extra day. Data was gathered for this event to avoid a situation where nothing was gathered for the project, because we had been informed by the Catapult Centre that the project had to finish by 31/12/16 (only 1 year after the original 3-year project start).

12 CORSAIR024: Delineation of floodwater in urban areas. In flood relief management, EA uses SAR images to detect flooding. Several organisations have developed systems to extract the flood extent from a SAR image - tend to work well in rural areas but poorly in urban areas - but very important to detect urban flooding. TerraSAR-X M θ O R h h 2 1 A N B Y C D Layover (AB) and shadow (CD) in a flooded street between adjacent buildings.

13 Object of project was to further develop/test existing near real-time algorithm for detecting flooding in rural and urban areas of high resolution SAR scene (Mason et al.2012). To detect urban flooding, algorithm uses SAR simulator in conjunction with LiDAR data. masks out areas not visible results in more accurate urban flood extent Method only tested on single SAR scene from single flood event, so not robust. Project has involved further testing on other events, and modification in light of results. Work is part of the EPSRC DARE project (Data assimilation for the Resilient City).

14 Thames flooding west of London in February 2014 Substantial urban areas flooded, especially around Wraysbury, Staines. Peak flow 404 m 3 /s on 11/02/2014 CSK images acquired under CORSAIR project 12/02/2014 (covers Wraysbury not Staines) 13/02/2014 (flow only 5% less than peak, covers Staines) 14 18/02/2014.

15 Aerial photo of flooding in Wraysbury, West London (about 300 x 300 m) CSK sub-image (1 x1 km) of Thames flood in Wraysbury (dark areas are water). Red outline shows the area covered by the aerial photo (COSMO-SkyMed Product - ASI [2014] processed under license from ASI Agenzia Spaziale Italiana. All rights reserved. Distributed by e-geos)..

16 SAR simulator: RaySAR To date used SETES SAR simulator, not publicly available First step was investigation of simulators Selected RaySAR (Auer 2011), based on POV-Ray POV-Ray is ray-tracer developed for use with incoherent visible light RaySAR extends POV-Ray to cope with coherent SAR ray-tracing RaySAR can model layover and shadow.

17 LiDAR DSM (lighter = higher) Shadow map (radar looking West, bright areas are shadow) Layover map (bright areas are layover)

18 Urban areas (white)

Fraction Catapult Open 1.2 1 0.8 0.6 0.4 0.

19 Fraction Catapult Open Total misclassified Misclassified water Misclassified non-water Pixel intensity threshold Tu Variation of misclassified water and non-water (high land) pixels with pixel intensity threshold T u Water (blue) and high land (red) training regions

20 Flood classification (blue) in rural areas after refinement, overlain on SAR image Local flood waterline height threshold calculated from rural flood map

21 Urban flood detection Guided by rural flood detection. Urban pixels classed as water seeds if SAR backscatter < backscatter threshold Heights < waterline height threshold Not in shadow/layover A weighted distance transform is used to grow the surviving seed into flood regions Grows flood regions preferentially along roads with low backscatter Grows also into shadow/layover areas Tries to compensate for limitation of urban flood detection using SAR that SAR can t see into shadow/layover

22 Extract from SAR image Correspondence between the SAR and aerial photograph flood extents in urban area of Wraysbury, superimposed on the LiDAR image (yellow = wet in SAR and aerial photos, red = wet in SAR only, green = wet in aerial photos only)

23 CORSAIR024: Conclusions Wraysbury just one of 3 test events processed. Average flood detection accuracy is 84%, with false alarm rate 20%. Conclude that flooding can be detected in the urban area to good accuracy, but that this accuracy is limited partly by the SAR s poor visibility of the urban ground surface due to shadow and layover.

DARE team and partners

DARE team and partners Complex and vulnerable city Miami Beach, Florida, September 2015 Manhattan after Superstorm Sandy, 2012. Photo: Allison Joyce/Getty Images. Chennai Airport, India, December 2015;