Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept

Abstract

Abstract. With the onset of new satellite radar constellations (e.g. Sentinel-1) and advances in computational science (e.g. grid computing) enabling the supply and processing of multimission satellite data at a temporal frequency that is compatible with real-time flood forecasting requirements, this study presents a new concept for the sequential assimilation of Synthetic Aperture Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models. The proposed methodology consists of adjusting storages and fluxes simulated by a coupled hydrologic-hydraulic model using a Particle Filterbased data assimilation scheme. Synthetic observations of water levels, representing satellite measurements, are assimilated into the coupled model in order to investigate the performance of the proposed assimilation scheme as a function of both accuracy and frequency of water level observations. The use of the Particle Filter provides flexibility regarding the form of the probability densities of both model simulations and remote sensing observations. We illustrate the potential of the proposed methodology using a twin experiment over a widely studied river reach located in the Grand-Duchy of Luxembourg. The study demonstrates that the Particle Filter algorithm leads to significant uncertainty reduction of water level and discharge at the time step of assimilation. However, updating the storages of the model only improves the model forecast over a very short time horizon. A more effective way of updating thus consists in adjusting both states and inputs. The proposed methodology, which consists in updatCorrespondence to: P. Matgen (matgen@lippmann.lu) ing the biased forcing of the hydraulic model using information on model errors that is inferred from satellite observations, enables persistent model improvement. The present schedule of satellite radar missions is such that it is likely that there will be continuity for SAR-based operational water management services. This research contributes to evolve reactive flood management into systematic or quasi-systematic SAR-based flood monitoring services.

A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation

“…Particle filter assimilation is a Bayesian learning system which accounts for input data uncertainty propagation by selecting suitable input data from randomly generated ones without assuming any particular distribution of their PDF (Noh et al, 2011). The particle filter technique was used in studies like Matgen et al (2010) and Giustarini et al (2011), where input data are in the form of ensemble flow outputs of a hydrological model. In Giustarini et al (2011) to assimilate water levels derived from two SAR images of flooding in the Alzette River into a hydraulic model, 64 upstream flows were generated from an ensemble hydrologic model and used as the upstream boundary conditions.…”

“…Barneveld et al (2008) applied the same method and models for flood forecasting on the Rhine River and produced good results of 10-day forecasts; therefore assimilating data for natural catchments results in better forecast model values. More information on hydrologic data assimilation techniques can be found in Matgen et al (2010), Chen et al (2013) and García-Pintado et al (2015).…”

Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

“…Though these studies have shown that the EnKF is a valuable tool for data assimilation in many applications, this study focuses on the use of the Particle Filter (PF). According to recent studies, the PF is an effective hydrologic and hydraulics data assimilation method, providing predictive uncertainty in model states, parameters and fluxes (DeChant and Moradkhani, 2011a, b;Matgen et al, 2010;Leisenring and Moradkhani, 2010;Moradkhani et al, 2005b;Montzka et al, 2011;Rings et al, 2010;Weerts and El Serafy, 2006). The PF is not subject to the limitations experienced in the EnKF including the Gaussian assumption of joint distribution of observation and model states and the linear updating of model states (Moradkhani et al, 2005b;Moradkhani and Sorooshian, 2008).…”

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

“…The potential of assimilating distributed value of water level from remote sensing for improved discharge and water depth estimation has been explored in different studies (e.g. Andreadis et al, 2007;Neal et al, 2007;Hostache et al, 2010;Matgen et al, 2010;Biancamaria et al, 2011;Giustarini et al, 2011;Mason et al, 2012;García-Pintado et al, 2013;Andreadis and Schumann, 2014) and a detailed review is presented by Schumann et al (2009) and Yan et al (2015).…”