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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 1
Hydrol. Earth Syst. Sci., 21, 635–650, 2017
https://doi.org/10.5194/hess-21-635-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Sub-seasonal to seasonal hydrological forecasting

Hydrol. Earth Syst. Sci., 21, 635–650, 2017
https://doi.org/10.5194/hess-21-635-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 31 Jan 2017

Research article | 31 Jan 2017

Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States

Chengcheng Huang1,2, Andrew J. Newman2, Martyn P. Clark2, Andrew W. Wood2, and Xiaogu Zheng1 Chengcheng Huang et al.
  • 1College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
  • 2National Center for Atmospheric Research, Boulder, CO 80301, USA

Abstract. In this study, we examine the potential of snow water equivalent data assimilation (DA) using the ensemble Kalman filter (EnKF) to improve seasonal streamflow predictions. There are several goals of this study. First, we aim to examine some empirical aspects of the EnKF, namely the observational uncertainty estimates and the observation transformation operator. Second, we use a newly created ensemble forcing dataset to develop ensemble model states that provide an estimate of model state uncertainty. Third, we examine the impact of varying the observation and model state uncertainty on forecast skill. We use basins from the Pacific Northwest, Rocky Mountains, and California in the western United States with the coupled Snow-17 and Sacramento Soil Moisture Accounting (SAC-SMA) models. We find that most EnKF implementation variations result in improved streamflow prediction, but the methodological choices in the examined components impact predictive performance in a non-uniform way across the basins. Finally, basins with relatively higher calibrated model performance (> 0.80 NSE) without DA generally have lesser improvement with DA, while basins with poorer historical model performance show greater improvements.

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This study examined the potential of snow water equivalent data assimilation to improve seasonal streamflow predictions. We examined aspects of the data assimilation system over basins with varying climates across the western US. We found that varying how the data assimilation system is implemented impacts forecast performance, and basins with good initial calibrations see less benefit. This implies that basin-specific configurations and benefits should be expected given this modeling system.
This study examined the potential of snow water equivalent data assimilation to improve seasonal...
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