SHARP is being designed to facilitate the demonstration and evaluation of variations in the real-time forecasting workflow for short, medium and seasonal range streamflow predictions. Check out current forecasts at the Over-The-Loop Streamflow Forecast Demonstration Project webpage.
To date, SHARP has centered on forecasting in selected medium-sized basins in the continental US. As the figure below illustrates, the philosophy behind SHARP recognizes that model-based streamflow forecasting depends critically on not only the hydrologic and associated models (e.g., river routing), but on a range of key methods and datasets including meteorological analyses, downscaling of weather and climate forecasts, model parameter estimation (ie, calibration), data assimilation, post-processing, and a robust capacity for verification and diagnostic evaluation.
Model Development Team
NCAR: Andy Wood (PI), Martyn Clark (Co-PI), Pablo Mendoza, Andy Newman, Ethan Gutmann
University of Washington: Bart Nijssen (PI), Elizabeth Clark
U.S. Army Corps of Engineers: Jeff Arnold
Bureau of Reclamation: Levi Brekke
Contact: Andy Wood - andywood@ucar.edu
Sponsors
Bureau of Reclamation, U.S. Army Corps of Engineers, and the National Oceanic and Atmospheric Administration
More Information At
Project information: streamflow_forecasting
Model homepage: https://www.ral.ucar.edu/projects/sharp
Papers:
Zhao, T., J. Bennett, Q.J. Wang, A. Schepen, A.W. Wood, D. Robertson, and M.H. Ramos, 2017: How suitable is quantile mapping for post-processing GCM precipitation forecasts?. J. Climate,
30,
3185-3196,
doi:10.1175/JCLI-D-16-0652.1
Wood, A.W.,T. Hopson,A. Newman, L.D. Brekke, J.R. Arnold, and M. Clark, 2016: Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill. Journal of Hydrometeorology,
17,
651-668,
doi:10.1175/JHM-D-14-0213.1
Pagano, T.C., F. Pappenberger, A.W. Wood, M.H. Ramos, A. Persson, and B. Anderson, 2016: Automation and human expertise in operational river forecasting. WIREs Water,
3,
692–705,
doi:10.1002/wat2.1163
Newman, A.J., M.P. Clark, J. Craig, B. Nijssen, A.W. Wood, E.D. Gutmann, N. Mizukami, L. Brekke, and J.R. Arnold, 2015: Gridded ensemble precipitation and temperature estimates for the contiguous United States. Journal of Hydrometeorology,
16,
2481-2500,
doi:10.1175/JHM-D-15-0026.1
Newman, A. J., M.P. Clark, K. Sampson, A.W. Wood, L.E. Hay, A. Bock, R.J. Viger, D. Blodgett, L. Brekke, J.R. Arnold, T. Hopson, and Q. Duan, 2015: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance. Hydrology and Earth System Sciences,
19,
209-223,
doi:10.5194/hess-19-209-2015
Emerton, R., E.M. Stephens, F. Pappenberger, T.C. Pagano, A.H. Weerts, A.W. Wood, P. Salamon, J.D. Brown, N. Hjerdt, C. Donnelly, and H.L. Cloke, 2015: Continental and Global Scale Flood Forecasting Systems. WIREs Water,
doi:10.1002/wat2.1137
Crochemore, L., M.H. Ramos, F. Pappenberger, S.J. van Andel, and A.W. Wood, 2015: An experiment on risk-based decision-making in water management using probabilistic forecasts. Bull. Amer. Met. Soc.,
doi:10.1175/BAMS-D-14-00270.1
Pagano, T.C., A.W. Wood, M.H. Ramos, H.L. Cloke, F. Pappenberger, V. Andréassian, M.P. Clark, M. Cranston, D. Kavetski, T. Mathevet, S. Sorooshian, and J.S. Verkade, 2014: Challenges of Operational River Forecasting. AMS J. Hydromet.,
15,
1692–1707,
doi:10.1175/JHM-D-13-0188.1
