A Benchmark for Probabilistic Seasonal Streamflow Forecasting over North America

Seasonal streamflow forecasts are critical for many different sectors. For instance, seasonal streamflow forecasts can be used to manage water supply, hydropower generation, and improve scheduling of irrigation. Initial hydrological conditions (e.g., snow storage and soil moisture) are an important source of hydrological predictability on seasonal timescales. Snow is the main source of runoff generation in high-latitude and/or high-altitude headwaters basins across North America, and the basins downstream. As a result, data-driven forecasting from snow observations is a well-established approach for operational seasonal streamflow forecasting in the USA and Canada.

The aim of this work is to benchmark probabilistic seasonal streamflow predictability across North America. To this end, we develop a data-driven probabilistic seasonal streamflow hindcasting workflow and implement it for basins with a nival regime across North America. The workflow uses snow water equivalent measurements from the recent update of the Canadian historical Snow Water Equivalent dataset (CanSWE), the Natural Resources Conservation Service (NRCS) manual snow surveys, and the SNOTEL automatic snow pillow in the USA. These datasets are gap filled using quantile mapping based on neighboring snow and precipitation stations. Principal Component Analysis is then used to define a small set of orthogonal predictor variables. These principal components are used as predictors in a regression model to generate ensemble hindcasts of streamflow volumes for basins across North America.

Preliminary results for 93 nival basins and 17 glacial basins across Canada suggest that this forecasting method has the ability to provide skilful hindcasts (i.e., better than streamflow climatology) during the snowmelt season. The results of this study provide a benchmark against which alternative forecasting methods (e.g., process-based forecasting models) can be assessed in the future.

This work is a contribution of the recently launched Cooperative Institute for Research to Operations in Hydrology (CIROH) initiative that aims to develop next-generation water prediction capabilities. The CIROH program and the Global Water Futures (GWF) program are advancing capabilities for probabilistic streamflow forecasting over North America.