Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts

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.

Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

“…Furthermore, Wood et al [53] used medium resolution SAR data to calibrate LISFLOOD-FP hydraulic model and to obtain the River Severn channel depth and friction parameters during several floods in the UK. Hostache et al [54] assimilated the satellite-derived flood extent maps into LISFLOOD-FP to reduce forecast uncertainties.…”

Flood Inundation Mapping of the Sparsely Gauged Large-Scale Brahmaputra Basin Using Remote Sensing Products

“…For this reason, different remote sensing products providing hydro-meteorological information were used for building, calibrating and validating the hydrological and hydraulic models. The authors decided to adopt a coupled hydrological and hydraulic modelling framework because it allows to simulate and predict flood extents at different scales as demonstrated by different studies (e.g., [34,35,69]). Two different sets of precipitation input (TRMM and TRMM corrected using bias correction) were used.…”

“…The results of our study proved that remote sensing information can be used for hydrological and hydraulic modelling to correctly determine areas which are prone to flooding and subsequently be used in flood risk management. In addition to model calibration and validation, remote sensing products of water level could be also used in data assimilation application for properly updating model states and/or parameters and improve model forecasting near-real time as recently demonstrated by Hostache et al [69] and Wood et al [70]. Assimilation of remote sensing of water level in hydraulic models is a non-trivial problem which is receiving growing attention in the last years.…”

A bivariable coupling model for river channel routing developed from the flow continuity equation and its application

“…Flood forecasting based on hydrological models is an important non-engineering measure for flood control and disaster reduction, which has received increasing attention from public, government and academic communities (Al-Safi and Sarukkalige, 2017;Yu et al, 2014). Efficient reservoir operation, river management, flood control and warning depend on reliable and accurate real-time flood forecasts (Cloke and Pappenberger, 2009;Hartnett and Nash, 2017;Si et al, 2015), which can be achieved by using hydrological models (Hostache et al, 2018). However, both conceptual models and distributed models need an accurate river flow routing module to calculate flood processes (Barati et al, 2012;Xu et al, 2017).…”