Potential in improving monthly streamflow forecasting through variational assimilation of observed streamflow

Uncertainties associated with the initial conditions (e.g. soil moisture content) of a hydrologic model have been recognized as one of the main sources of errors in hydrologic predictions. Recent advances in sequential Data Assimilation (DA) and variational DA (VAR DA) have focused on correcting these initial conditions for improving hydrologic predictions. This study proposes a VAR DA methodology that considers a basin-wide single scaling/correction factor for updating the soil moisture content of Variable Infiltration Capacity (VIC) Land Surface Model (LSM) by assimilating gauge-observed streamflow. This simplified scaling factor reduces the computational demand of the VAR DA in updating the soil moisture conditions of VIC. The proposed VAR DA scheme is demonstrated in Tar River Basin in NC,a rainfall-runoff dominated watershed, for improving monthly streamflow simulations and forecasting over a 20-year period (1991–2010). The role of two critical parameters of VAR DA – the update frequency (the interval between DA applications) and the length of assimilation window – in determining the skill of DA-improved streamflow predictions is also assessed. We found that correcting VIC model's initial conditions using a 7-day assimilation window results in the highest improvement in the skill of streamflow predictions quantified by Kling-Gupta Efficiency (KGE) and Nash–Sutcliffe Efficiency (NSE) metrics. In addition, the potential gain from VAR DA framework is quantified and compared under two 1-month ahead streamflow forecasting schemes: 1) VAR DA corrected initial conditions of VIC forced with ECHAM4.5 GCM 1-month ahead precipitation forecasts and 2) Ensemble Streamflow Prediction (ESP) approach. This study also examines the persistence of the DA in improving monthly streamflow predictions by quantifying the enhanced accuracy in daily flows over extended forecast lead time blocks. Analyses show that the corrected initial state conditions continually enhance the 7–8 days ahead streamflow predictions, but after that the errors in forcings dominate the DA effects. Overall, the application of proposed VAR DA scheme results in improved monthly streamflow forecasting due to correcting initial conditions.