Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale

Abstract

AbstractSkillful streamflow forecasts provide key support to several water‐related applications. Because of the critical impact of initial conditions (ICs) on forecast accuracy, ever‐growing interest is focused on improving their estimates via data assimilation (DA). This study aims to assess the sensitivity of the DA‐based estimation of forecast ICs to several sources of uncertainty and the update of different model states and parameters of a lumped conceptual rainfall–runoff model over 232 watersheds in France. The performance of two sequential ensemble‐based techniques, namely, the ensemble Kalman filter (EnKF) and the particle filter (PF), is compared in terms of efficiency and temporal persistence (up to 10 days) of the updating effect through the assimilation of observed discharges. Several experiments specifically address the impact of the meteorological, state, and parameter uncertainties. Results show that an accurate estimate of the initial level of the routing store of the conceptual model ensures the most benefit to the DA‐based estimation of forecast ICs. While EnKF‐based forecasts outperform PF‐based ones when accounting for meteorological uncertainty, the more comprehensive representation of the state uncertainty makes it possible to greatly improve the accuracy of PF‐based predictions, with a longer‐lasting updating effect. Conversely, forecasting skill is undermined when accounting for parameter uncertainty, owing to the change in hydrological responsiveness. This study extensively addresses several sensitivity analyses in order to provide useful recommendations for designing DA‐based streamflow forecasting systems and for diagnosing possible deficiencies in existing systems.