Articles | Volume 22, issue 3
https://doi.org/10.5194/hess-22-1831-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-1831-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system
Sanjib Sharma
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
Ridwan Siddique
Northeast Climate Science Center, University of Massachusetts, Amherst, MA, USA
Seann Reed
National Weather Service, Middle Atlantic River Forecast Center, State College, PA, USA
Peter Ahnert
National Weather Service, Middle Atlantic River Forecast Center, State College, PA, USA
Pablo Mendoza
Advanced Mining Technology Center (AMTC), Universidad de Chile, Santiago, Chile
Alfonso Mejia
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
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- Hydrological Model Diversity Enhances Streamflow Forecast Skill at Short‐ to Medium‐Range Timescales S. Sharma et al. 10.1029/2018WR023197
- Multiscale Postprocessor for Ensemble Streamflow Prediction for Short to Long Ranges B. Alizadeh et al. 10.1175/JHM-D-19-0164.1
- Multi-Spatial Resolution Rainfall-Runoff Modelling—A Case Study of Sabari River Basin, India V. Sharma & S. Regonda 10.3390/w13091224
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- Climate Change Impacts on Local Flood Risks in the U.S. Northeast: A Case Study on the Connecticut and Merrimack River Basins R. Siddique & R. Palmer 10.1111/1752-1688.12886
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- On the selection of precipitation products for the regionalisation of hydrological model parameters O. Baez-Villanueva et al. 10.5194/hess-25-5805-2021
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Latest update: 20 Nov 2024
Short summary
We investigate the relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7). For this purpose, we develop and implement a regional hydrologic ensemble prediction system (RHEPS). Overall analysis shows that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.
We investigate the relative roles of statistical weather preprocessing and streamflow...