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

Huang, Chengcheng; Newman, Andrew J.; Clark, Martyn P.; Wood, Andrew W.; Zheng, Xiaogu

doi:https://doi.org/10.5194/hess-21-635-2017

Articles | Volume 21, issue 1

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

https://doi.org/10.5194/hess-21-635-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 21, issue 1

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 Huang, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, and Xiaogu Zheng

Supplement

https://doi.org/10.5194/hess-21-635-2017-supplement

Data sets

A large-sample watershed-scale hydrometeorological dataset for the contiguous USA, Boulder, CO: UCAR/NCAR A. Newman, K. Sampson, M. P. Clark, A. Bock, R. J. Viger, and D. Blodgett https://doi.org/10.5065/D6MW2F4D

Gridded Ensemble Precipitation and Temperature Estimates over the Contiguous United States, Boulder, CO: UCAR/NCAR-CISL-CDP A. J. Newman, M. P. Clark, J. Craig, B. Nijssen, A. Wood, and E. Gutmann https://doi.org/10.5065/D6TH8JR2

Short summary

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.