Improving statistical forecasts of seasonal streamflows using hydrological model output

Abstract

Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record

A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions

“…Reliability refers to "statistical consistency" of the predictive probability distributions with the observed frequency of the events (Toth et al, 2003;Robertson et al, 2012). In this study, we use PIT (probability integral transform) uniform probability plots (Wang et al, 2009; to assess the overall reliability of the post-processed predictive distributions.…”

A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions

“…Reliability refers to "statistical consistency" of the predictive probability distributions with the observed frequency of the events (Toth et al, 2003;Robertson et al, 2012). In this study, we use PIT (probability integral transform) uniform probability plots to assess the overall reliability of the post-processed predictive distributions.…”