Quantifying streamflow predictability across North America on sub-seasonal to seasonal timescales

Abstract:

Sub-seasonal to seasonal (S2S) streamflow forecasts currently represent critical operational inputs for many water sector applications of societal relevance, including spring flood early warning, water supply and 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-domain forecasting system that has value at the local scale, the sources and nature of predictability in the forecasts should be quantified and communicated. This additionally helps to target science investments that can tangibly improve the skill of S2S streamflow forecasts.

As part of the Global Water Futures (GWF) program in Canada, we are advancing capabilities for probabilistic S2S streamflow forecasts over North America. Our work encompasses a range of forecasting methods that integrate state-of-the-art mechanistic models and statistical methods. These include a probabilistic S2S streamflow forecasting system based on quantile regression of snow water equivalent observations. To guide forecasting system development over North America, we are currently quantifying streamflow predictability for different hydroclimatic regimes, forecast initialization times, and forecast lead times. Building on the work done by Arnal et al. (2017), we are disentangling the dominant predictability sources (i.e. initial hydrological conditions and atmospheric forcings) of S2S 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 forecasting system, and will inform the continental-domain forecasting system development. The overall aim is to improve S2S streamflow forecasts for a range of water sector applications.

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