Quantification of the forecast uncertainty using conditional probability and updating models

Abstract

Abstract Quantifying forecast uncertainty is of great importance for reservoir operation and flood control. However, deterministic hydrological forecasts do not consider forecast uncertainty. This study develops a conditional probability model based on copulas to quantify forecast uncertainty. Three updating models, namely auto-regressive (AR) model, AR exogenous input model, and adaptive neuro fuzzy inference system model, are applied to update raw deterministic inflow forecasts of the Three Gorges Reservoir on the Yangtze River, China with lead times of 1d, 2d, and 3d. Results show that the conditional probability model provides a reasonable and reliable forecast interval. The updating models both enhance the forecast accuracy and improve the reliability of probabilistic forecasts. The conditional probability model based on copula functions is a useful tool to describe and quantify forecast uncertainty, and using an updating model is an effective measure to improve the accuracy and reliability of probabilistic forecast.