Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts. Issue 8 (17th August 2018)

• Record Type: Journal Article
• Authors: Hostache, Renaud; Chini, Marco; Giustarini, Laura; Neal, Jeffrey; Kavetski, Dmitri; Wood, Melissa; Corato, Giovanni; Pelich, Ramona‐Maria; Matgen, Patrick
• Is Part Of: Water resources research. Volume 54:Issue 8(2018)
• Page Start: 5516
• Page End: 5535
• Publication Date: 2018-08-17
• Subjects: flood forecasting -- data assimilation -- SAR image -- flood extent -- hydrological modeling -- hydraulic modeling; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2017WR022205
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Short‐ to medium‐range flood forecasts are central to predicting and mitigating the impact of flooding across the world. However, producing reliable forecasts and reducing forecast uncertainties remains challenging, especially in poorly gauged river basins. The growing availability of synthetic aperture radar (SAR)‐derived flood image databases (e.g., generated from SAR sensors such as Envisat advanced synthetic aperture radar) provides opportunities to improve flood forecast quality. This study contributes to the development of more accurate global and near real‐time remote sensing‐based flood forecasting services to support flood management. We take advantage of recent algorithms for efficient and automatic delineation of flood extent using SAR images and demonstrate that near real‐time sequential assimilation of SAR‐derived flood extents can substantially improve flood forecasts. A case study based on four flood events of the River Severn (United Kingdom) is presented. The forecasting system comprises the SUPERFLEX hydrological model and the Lisflood‐FP hydraulic model. SAR images are assimilated using a particle filter. To quantify observation uncertainty as part of data assimilation, we use an image processing approach that assigns each pixel a probability of being flooded based on its backscatter values. Empirical results show that the sequential assimilation of SAR‐derived flood extent maps leads to a substantial improvement in water level forecasts. Forecast errors are reduced by as much as 50% at the assimilation time step, and improvements persist over subsequent time steps for 24 to 48 hr. The proposed approach holds promise for improving flood forecasts at locations where observed data availability is limited but satellite coverage exists. Key Points: Probabilistic flood maps are derived from SAR images Probabilistic flood maps are assimilated into a flood forecasting model cascade Water level forecast quality improves substantially in the assimilation time steps, and benefits persist for hours to days