Estimating the impact of satellite observations on the predictability of large-scale hydraulic models

• We adapted a method of estimating the observation impact on river hydrodynamic forecasting.
• Observations had a mostly positive impact on forecasts of WSE for lead times of 1–11 days.
• Discharge forecasts were improved by assimilating either WSE or top width observations.
• We discussed how this method could be used to identify model structural or parameter errors.

Large-scale hydraulic models are able to predict flood characteristics, and are being used in forecasting applications. In this work, the potential value of satellite observations to initialize hydraulic forecasts is explored, using the Ensemble Sensitivity method. The impact estimation is based on the Local Ensemble Transform Kalman Filter, allowing for the forecast error reductions to be computed without additional model runs. The experimental design consisted of two configurations of the LISFLOOD-FP model over the Ohio River basin: a baseline simulation represents a “best effort” model using observations for parameters and boundary conditions, whereas the second simulation consists of erroneous parameters and boundary conditions. Results showed that the forecast skill was improved for water heights up to lead times of 11 days (error reductions ranged from 0.2 to 0.6 m/km), while even partial observations of the river contained information for the entire river’s water surface profile and allowed forecasting 5 to 7 days ahead. Moreover, water height observations had a negative impact on discharge forecasts for longer lead times although they did improve forecast skill for 1 and 3 days (up to 60 m3/s/kmm3/s/km). Lastly, the inundated area forecast errors were reduced overall for all examined lead times. Albeit, when examining a specific flood event the limitations of predictability were revealed suggesting that model errors or inflows were more important than initial conditions.