Observation operators for assimilation of satellite observations in fluvial inundation forecasting

Abstract

Abstract. Images from satellite-based synthetic aperture radar (SAR) instruments contain large amounts of information about the position of floodwater during a river flood event. This observational information typically covers a large spatial area but is only relevant for a short time if water levels are changing rapidly. Data assimilation allows us to combine valuable SAR-derived observed information with continuous predictions from a computational hydrodynamic model and thus to produce a better forecast than using the model alone. In order to use observations in this way, a suitable observation operator is required. In this paper we show that different types of observation operators can produce very different corrections to predicted water levels; this impacts the quality of the forecast produced. We discuss the physical mechanisms by which different observation operators update modelled water levels and introduce a novel observation operator for inundation forecasting. The performance of the new operator is compared in synthetic experiments with that of two more conventional approaches. The conventional approaches both use observations of water levels derived from SAR to correct model predictions. Our new operator is instead designed to use backscatter values from SAR instruments as observations; such an approach has not been used before in an ensemble Kalman filtering framework. Direct use of backscatter observations opens up the possibility of using more information from each SAR image and could potentially speed up the time taken to produce observations needed to update model predictions. We compare the strengths and weaknesses of the three different approaches with reference to the physical mechanisms with which each of the observation operators allow data assimilation to update water levels in synthetic twin experiments in an idealised domain.

Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept

“…In our study none of the model state variables is updated as only the particle weights are computed, based on the SAR observations and on the simulated flood extent maps, and used to calculate the expectation of water levels and streamflow. In previous studies (Andreadis et al, 2007;Matgen et al, 2010;Cooper et al, 2019), inflow updating was identified as a condition leading to more persistent improvements. For instance, one of the conclusions from the study by Matgen et al (2010) was that updating the fluxes at the upstream boundary conditions, rather than the water levels, is more effective because of the high uncertainty of the inflow due to the poorly known rainfall distribution over the catchment.…”

“…In the existing literature only a few studies have used DA for directly assimilating flood extent maps into flood forecasting models (e.g., Lai et al, 2014;Revilla-Romero et al, 2016;Cooper et al, 2019Cooper et al, , 2018Hostache et al, 2018). Among the advantages of a direct use of the SAR backscatter values is that it reduces the data processing time, which is a key element in near-operational applications.…”

Assimilation of probabilistic flood maps from SAR data into ahydrologic-hydraulic forecasting model: a proof of concept

“…as reported in Cooper et al (2019). The nearly-direct use of the SAR information has the additional advantage of a faster processing from the acquisition to the assimilation of the SAR image being therefore suitable for operational needs.…”

Floodwater detection in urban areas using Sentinel-1 and WorldDEM data

“…5 A second use is to provide near real-time data for assimilation into urban flood inundation models, to correct the model state, and improve estimates of the model parameters and external forcing. [6][7][8][9][10][11][12][13] A substantial amount of research has been carried out on automated flood detection in rural areas. [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Several organisations, including the Copernicus Emergency Management Service (EMS), have developed semi-automatic systems to extract the flood extent from an SAR image.…”