Characterizing the uncertainty in river stage forecasts conditional on point forecast values

Abstract

Uncertainty information about river level forecast is as important as the forecast itself for forecast users. This paper presents a flexible, statistical approach that processes deterministic forecasts into probabilistic forecasts. The model is a smoothly changing conditional distribution of river stage given point forecast and other information available, such as lagged river level at the time of forecasting. The parametric distribution is a four-parameter skew t distribution, with each parameter modeled as a smooth function of the point forecast and the 1 day ago observed river level. The model was applied to 9 years of daily 6 h lead forecasts and 24 h lead forecasts in the warm season and their matching observations at the Plymouth station on the Pemigewasset River in New Hampshire. For each point forecast, the conditional distribution and resulting prediction intervals provide uncertainty information that are potentially very important to forecast users and algorithm developers in decision making and improvement of forecast quality. © 2012. American Geophysical Union. All Rights Reserved.