Satellite-supported flood forecasting in river networks: A real case study

Abstract

s u m m a r ySatellite-based (e.g., Synthetic Aperture Radar [SAR]) water level observations (WLOs) of the floodplain can be sequentially assimilated into a hydrodynamic model to decrease forecast uncertainty. This has the potential to keep the forecast on track, so providing an Earth Observation (EO) based flood forecast system. However, the operational applicability of such a system for floods developed over river networks requires further testing. One of the promising techniques for assimilation in this field is the family of ensemble Kalman (EnKF) filters. These filters use a limited-size ensemble representation of the forecast error covariance matrix. This representation tends to develop spurious correlations as the forecast-assimilation cycle proceeds, which is a further complication for dealing with floods in either urban areas or river junctions in rural environments. Here we evaluate the assimilation of WLOs obtained from a sequence of real SAR overpasses (the X-band COSMO-Skymed constellation) in a case study. We show that a direct application of a global Ensemble Transform Kalman Filter (ETKF) suffers from filter divergence caused by spurious correlations. However, a spatially-based filter localization provides a substantial moderation in the development of the forecast error covariance matrix, directly improving the forecast and also making it possible to further benefit from a simultaneous online inflow error estimation and correction. Additionally, we propose and evaluate a novel along-network metric for filter localization, which is physically-meaningful for the flood over a network problem. Using this metric, we further evaluate the simultaneous estimation of channel friction and spatially-variable channel bathymetry, for which the filter seems able to converge simultaneously to sensible values. Results also indicate that friction is a second order effect in flood inundation models applied to gradually varied flow in large rivers. The study is not conclusive regarding whether in an operational situation the simultaneous estimation of friction and bathymetry helps the current forecast. Overall, the results indicate the feasibility of standalone EO-based operational flood forecasting.

Technical note: Assessment of observation quality for data assimilation in flood models

“…This study makes use of the observation, model and assimilation system described in García-Pintado et al (2015). We direct the reader to this reference, and references therein (particularly Mason et al, 2012a, b), for a thorough description of the derivation of WLOs and the assimilation design.…”

“…Due to the formulation with the diagnostic described in Sect. 2, the localization in the LETKF is set in standard 2-D Euclidean space rather than the physically based distance along the river channel described in García-Pintado et al (2015), which would require a further adaptation of the diagnostic calculation. The localization radius is set using a compactly supported fifthorder piecewise rational function (Gaspari and Cohn, 1999, Eq.…”

“…On 30 November the river went back in bank; however, a substantial amount of water remained on the floodplain (see Fig. 2 in García-Pintado et al, 2015).…”

Calibration of channel depth and friction parameters in the LISFLOOD-FP hydraulic model using medium-resolution SAR data and identifiability techniques

“…Roux and Dartus (2008), for example, found sensitivity in hydraulic model response to channel roughness to be weaker than sensitivity to geometry parameters and boundary conditions within a generalised sensitivity analysis framework. Additionally, Garcia-Pintado et al (2015) found that sensitivity to bathymetry parameters dominated when using the ensemble transform Kalman filter to simultaneously estimate bathymetry and channel friction. The sensitivity in channel friction may therefore be not as obvious when other parameters are simultaneously calibrated because the model is no longer compensating for previously unrepresented uncertainties.…”

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

“…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).…”