Statistical applications of physically based hydrologic models to seasonal streamflow forecasts

• Published in: Water resources research 2011-03, Vol.47 (3), p.n/a
• Main Authors: Rosenberg, Eric A., Wood, Andrew W., Steinemann, Anne C.
• Format: Article
• Language: English
• Subjects: California; Communication; Drainage; Forecasting techniques; hydrologic modeling; Hydrologic models; Hydrology; Infiltration capacity; Meteorology; Natural resources; Precipitation; Resource conservation; Rivers; seasonal streamflow; Snow; Snow-water equivalent; statistical forecasts; Statistical methods; Stream discharge; Stream flow; Streamflow forecasting; Water resources; Water shortages; Water supply; Watersheds; Weather forecasting
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1029/2010WR010101

Despite advances in physically based hydrologic models and prediction systems, long‐standing statistical methods remain a fundamental component in most operational forecasts of seasonal streamflow. We develop a hybrid framework that employs gridded observed precipitation and model‐simulated snow water equivalent (SWE) data as predictors in regression equations adapted from an operational forecasting environment. We test the modified approach using the semidistributed variable infiltration capacity hydrologic model in a case study of California's Sacramento River, San Joaquin River, and Tulare Lake hydrologic regions. The approach employs a principal components regression methodology, adapted from the Natural Resources Conservation Service, which leverages the ability of the distributed model to provide an added dimension to SWE predictors in a statistical framework. Hybrid forecasts based on data simulated at grid points acting as surrogates for ground‐based observing stations are found to perform comparably to those based on their observed counterparts. When a larger selection of grid points are considered as potential predictors, hybrid forecasts achieve superior skill, with the largest benefits in watersheds that are poorly represented in terms of ground‐based observations. Forecasts are also found to offer overall improvement over those officially issued by California's Department of Water Resources, although their specific performance in dry years is less consistent. The study demonstrates the utility of physically based models within an operational statistical framework, as well as the ability of the approach to identify locations with strong predictive skill for potential ground station implementation. Key Points Physically based models can be leveraged for operational statistical forecasts A hybrid approach permits late‐season forecasts when observing stations are snow‐free Analyses of distributed data sets can be used to locate new observing stations