Remote Sensing Data Assimilation with a Chained Hydrologic-hydraulic Model for Flood Forecasting
arxiv(2024)
摘要
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.
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