1. Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale.
- Author
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Piazzi, G., Thirel, G., Perrin, C., and Delaigue, O.
- Subjects
CONCEPTUAL models ,STREAM measurements ,MODELS & modelmaking ,FIX-point estimation ,UNCERTAINTY - Abstract
Skillful 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. Plain Language Summary: Accurate streamflow forecasts are of critical importance to several real‐time applications, such as water resource management and flood prevention. The predictive accuracy critically depends on the quality of the forecast initial conditions. Data assimilation (DA) techniques are increasingly being implemented to obtain the most likely estimation of forecast initial conditions through the assimilation of observed hydrological variables. This study compares the performances of two DA techniques, namely the Ensemble Kalman filter and the Particle filter, in terms of both efficiency and temporal persistence (up to 10 days) of the updating effect. The analysis addresses the impact of different sources of uncertainty and the update of different model states and parameters of a lumped conceptual hydrological model, when assimilating observed discharges over 232 watersheds in France. Results show that an accurate estimation of the initial level of the routing store ensures the most benefit of DA, as this state variable is the most correlated with observations. A comprehensive representation of the state uncertainty generally improves the estimation of the forecast initial conditions resulting from the assimilation. While the Ensemble Kalman filter outperforms the Particle filter in the short term, this latter guarantees a longer‐lasting updating effect over the forecast horizon. Key Points: Estimation of forecast initial level of the routing store of a conceptual hydrological model ensures the most benefit from data assimilationUpdating forecast initial conditions using the ensemble Kalman filter results in a greater improvement in predictive accuracy in the short termThe particle filter guarantees a longer‐lasting effect of the update of initial conditions over the forecast horizon [ABSTRACT FROM AUTHOR]
- Published
- 2021
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