Remote Sensing Data Assimilation with a Chained Hydrologic-hydraulic Model for Flood Forecasting

TL;DR

This paper presents a real-time flood forecasting framework that combines hydrologic and hydraulic models with remote sensing data assimilation, improving flood extent and water level predictions despite uncertainties in forcing data.

Contribution

It introduces a chained hydrologic-hydraulic modeling approach with a novel data assimilation strategy that effectively integrates remote sensing observations for flood forecasting.

Findings

Data assimilation improves reanalysis accuracy in hindcast mode.

Combining observed discharge with hydrologic model outputs yields the best forecast accuracy.

Forecast quality diminishes with increasing lead time and non-stationary forcing errors.

Abstract

A chained hydrologic-hydraulic model is implemented using predicted runoff from a large-scale hydrologic model (namely ISBA-CTRIP) as inputs to local hydrodynamic models (TELEMAC-2D) to issue forecasts of water level and flood extent. The uncertainties in the hydrological forcing and in friction parameters are reduced by an Ensemble Kalman Filter that jointly assimilates in-situ water levels and flood extent maps derived from remote sensing observations. The data assimilation framework is cycled in a real-time forecasting configuration. A cycle consists of a reanalysis and a forecast phase. Over the analysis, observations up to the present are assimilated. An ensemble is then initialized from the last analyzed states and issued forecasts for next 36 hr. Three strategies of forcing data for this forecast are investigated: (i) using CTRIP runoff for reanalysis and forecast, (ii) using observed discharge for analysis, then CTRIP runoff for forecast and (iii) using observed discharge for reanalysis and keep a persistent discharge value for forecast. It was shown that the data assimilation strategy provides a reliable reanalysis in hindcast mode. The combination of observed discharge and CTRIP runoff provides the most accurate results. For all strategies, the quality of the forecast decreases as the lead time increases. When the errors in CTRIP forcing are non-stationary, the forecast capability may be reduced. This work demonstrates that the forcing provided by a hydrologic model, while imperfect, can be efficiently used as input to a hydraulic model to issue reanalysis and forecasts, thanks to the assimilation of in-situ and remote sensing observations.