Model-based probabilistic seasonal streamflow forecasts

GeoNetwork record: www.gwfnet.net/geonetwork/srv/eng/catalog.search#/metadata/a4e7ab69-47c3-4cbd-8eba-e78ff0574ecb

Historical gridded meteorological forcings (Newmann et al., 2015), pre-processed with MetSim (Bennett et al., 2020), were used as inputs to the SUMMA hydrological model (Clark et al., 2015), which had been previously calibrated for the Kootenay River at Fort Steele. We use the ESP (ensemble streamflow prediction; Day, 1985) approach, whereby each historical year of meteorological observations is used to simulate a new future seasonal streamflow trace, producing a full dataset of probabilistic seasonal streamflow hindcasts. Hindcasts for the Kootenay River at Fort Steele were run on the Cheyenne NCAR (National Center for Atmospheric Research, Boulder, Colorado, US) supercomputer. References: Bennett, A., Hamman, J., & Nijssen, B. (2020). MetSim: A Python package for estimation and disaggregation of meteorological data. Journal of Open Source Software, 5(47), 2042, https://doi.org/10.21105/joss.02042 Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., & Arnold, J. R. (2015). A unified approach for process‐based hydrologic modeling: 1. Modeling concept. Water Resources Research, 51(4), 2498-514, https://doi.org/10.1002/2015WR017198 Day, G. N. (1985). Extended streamflow forecasting using NWSRFS. J. Water Res. Plan. Man., 111, 157–170, doi:10.1061/(ASCE)0733-9496(1985)111:2(157) Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., & Duan, Q. (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. Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015

This project supports the hydrological forecasting theme of the GWF Core Modelling and Forecasting Team. As part of the Global Water Futures project, the computational hydrology group builds tools to simulate and predict hydrologic processes. This work focuses on the last point and aims to: -Set up a North America-wide sub-seasonal to seasonal ensemble streamflow forecasting system -Assess the predictability of streamflow on seasonal timescales across North America

Case study: Kootenay River at Fort Steele. The long-term plan is to produce forecasts for river basins across North America.

Model name: SUMMA Model version number: 3.0 Model source/webpage: https://github.com/ncar/summa Model setup: Time step: daily Spatial resolution: Routed streamflow for each basin hydrological response unit (229 in total for the Kootenay River at Fort Steele)