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.
Research article
| 
13 Mar 2018
Research article |
| 13 Mar 2018
| 13 Mar 2018
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
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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
- Pre- and postprocessing flood forecasts using Bayesian model averaging T. Hegdahl et al. 10.2166/nh.2023.024
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- Selecting components in a probabilistic hydrological forecasting chain: the benefits of an integrated evaluation J. Bellier et al. 10.1080/27678490.2021.1936825
- A Hydraulic MultiModel Ensemble Framework for Visualizing Flood Inundation Uncertainty C. Zarzar et al. 10.1111/1752-1688.12656
- A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context L. Berthet et al. 10.5194/hess-24-2017-2020
- Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach S. Monhart et al. 10.5194/hess-23-493-2019
- Ensemble forecasting system for the management of the Senegal River discharge: application upstream the Manantali dam D. Ndione et al. 10.1007/s13201-020-01199-y
- Calibration of ECMWF SEAS5 based streamflow forecast in Seasonal hydrological forecasting for Citarum river basin, West Java, Indonesia D. Ratri et al. 10.1016/j.ejrh.2022.101305
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- Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region G. Ghimire et al. 10.3390/forecast2030013
- 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
- Quantifying the contributions of hydrological pre-processor, post-processor, and data assimilator to ensemble streamflow prediction skill J. Zhang et al. 10.1016/j.jhydrol.2024.132611
- Multi-Spatial Resolution Rainfall-Runoff Modelling—A Case Study of Sabari River Basin, India V. Sharma & S. Regonda 10.3390/w13091224
- Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting Z. Shu et al. 10.1016/j.eng.2022.06.007
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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
1 citations as recorded by crossref.
Latest update: 30 Jan 2025
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...
