Abstract
Satellite-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 stand-alone EO-based operational flood forecasting.
Highlights
While there are recent advances in low-cost telemetered networks for long-life flood monitoring and warning applications, oriented to be deployed over large areas (e.g., Marín-Pérez et al, 2012), the actual number of operational gauges is declining in the world (Vörösmarty et al, 2001)
Our objective is to evaluate a number of strategies for real-time flood forecasting by assimilating high-resolution EObased WLOs with the flood simulations assuming uncertain model parameters
We focus on filter configurations with simultaneous friction and/or bathymetry estimation. We evaluate if these parameters can be simultaneously estimated, and if this simultaneous estimation leads to an improvement in the flood forecast
Summary
While there are recent advances in low-cost telemetered networks for long-life flood monitoring and warning applications, oriented to be deployed over large areas (e.g., Marín-Pérez et al, 2012), the actual number of operational gauges is declining in the world (Vörösmarty et al, 2001). In recent times, the technology of earth observation (EO) has begun to be adopted to improve flood visualization and reduce flood. EO techniques for flood detection include, for example, high resolution Synthetic Aperture Radar (SAR) (such as TerraSAR-X), altimetry (such as RA-2 on Envisat, or Poseidon 3 on Jason-2), though the footprints are such that they are limited to level measurements in rivers >1 km wide, or even gravimetry (GRACE) for very large flood events. In real-time mode, the assimilation of water level observations (WLOs) derived from EO may serve to keep forecasts obtained from flood simulations on track and, in hindcast mode, to obtain better estimates of the dynamic footprints of past flood events. The forecast mode may be used by civil protection services and industry for operational uses, while the post-flood mode may be used in damage assessment and flood defence design studies
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