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<p><span><span>Sub-seasonal to seasonal streamflow forecasts represent critical operational inputs for many water sector applications of societal relevance, such as spring flood early warning, water supply, hydropower generation, and irrigation scheduling. However, the skill of such forecasts has not risen greatly in recent decades despite recognizable advances in many relevant capabilities, including hydrologic modeling and S2S climate prediction. In order to build a continental-scale forecasting system that has value at the local scale, the sources and nature of predictability in the forecasts should be quantified and communicated. This can additionally help to target science investments for tangible improvements in the sub-seasonal to seasonal streamflow forecasting skill.</span></span></p>
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<p><span><span>As part of the Canada-based Global Water Futures (GWF) program, we are advancing capabilities for probabilistic sub-seasonal to seasonal streamflow forecasts over North America. The overall aim is to improve sub-seasonal to seasonal streamflow forecasts for a range of water sector applications. We are implementing an array of forecasting methods that integrate state-of-the-art mechanistic models and statistical methods. These include, for instance, a </span></span><span>probabilistic sub-seasonal to seasonal streamflow forecasting system based on quantile regression of snow water equivalent observations, and a system based on the ESP approach (Day, 1985). </span></p>
<p><span><span>To guide forecast system developments over North America, we are currently quantifying streamflow predictability for different hydroclimatic regimes, forecast initialization times, and lead times, against both streamflow simulations and observations to quantify the effect of model errors. Building on the work from Wood et al. (2016) and Arnal et al. (2017), we are disentangling the dominant predictability sources (i.e., initial hydrological conditions and atmospheric forcings) of sub-seasonal to seasonal streamflow across North American watersheds. The results provide insights into the elasticity of predictability, i.e., the increase in streamflow forecast skill possible by improving a specific component of the forecast system, and will inform the forecasting system development.</span></span></p>
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<p><span><span>Arnal Louise, Wood Andrew W., Stephens Elisabeth, Cloke Hannah L., Pappenberger Florian, 2017: An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity. Journal of Hydrometeorology, doi: 10.1175/JHM-D-16-0259.1</span></span></p>
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<p>Day, Gerald N., 1985: Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management, doi:10.1061/(ASCE)0733-9496(1985)111:2(157)</p>
</div><p>Wood, Andrew W., Tom Hopson, Andy Newman, Levi Brekke, Jeff Arnold, and Martyn Clark, 2016: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. Journal of Hydrometeorology, doi: 10.1175/JHM-D-14-0213.1</p>
Improving statistical forecasts of seasonal streamflows using hydrological model output
