Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

C M Dechant,H Moradkhani

doi:10.5194/hess-15-3399-2011

Abstract

Abstract. Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations.

Highlights

Accurate prediction of seasonal streamflow information is essential to effectively managing surface water supply
Several studies in the past decade have applied this technique to hydrologic models (Andreadis and Lettenmaier, 2006; Durand et al, 2009; Moradkhani et al, 2005a; Reichle et al, 2002; Roddell and Houser, 2004; Slater and Cark, 2006; Sun et al, 2004; Zaitchik and Rodell, 2009). Though these studies have shown that the Ensemble Kalman Filter (EnKF) is a valuable tool for data assimilation in many applications, this study focuses on the use of the Particle Filter (PF)
This study examined the utility of incorporating ensemble data assimilation techniques to improve state initialization in the Ensemble Streamflow Prediction (ESP) framework by allowing for uncertainty in the initial condition

Summary

Introduction

Accurate prediction of seasonal streamflow information is essential to effectively managing surface water supply. For this reason, recent studies have examined techniques that have potential to provide skillful predictions of seasonal runoff volume (Kennedy et al, 2009; Moradkhani and Meier, 2010; Regonda et al, 2006; Thirel et al, 2008). Given that there is accurate quantification of the snow water storage in a specified region, this information can be utilized to improve the accuracy of seasonal streamflow prediction. This idea is implemented in the National Weather Service River Forecast System (NWSRFS) through Ensemble Streamflow Prediction (ESP)

Methods

Results

Conclusion