Abstract
Abstract. Hydrologic model predictions are often biased and subject to heteroscedastic errors originating from various sources including data, model structure and parameter calibration. Statistical post-processors are applied to reduce such errors and quantify uncertainty in the predictions. In this study, we investigate the use of a statistical post-processor based on the Bayesian joint probability (BJP) modelling approach to reduce errors and quantify uncertainty in streamflow predictions generated from a monthly water balance model. The BJP post-processor reduces errors through elimination of systematic bias and through transient errors updating. It uses a parametric transformation to normalize data and stabilize variance and allows for parameter uncertainty in the post-processor. We apply the BJP post-processor to 18 catchments located in eastern Australia and demonstrate its effectiveness in reducing prediction errors and quantifying prediction uncertainty.
Highlights
Streamflow predictions from a hydrological model can be used for wide range of applications including flood forecasting at short time scales to long-term assessments of water resources
We present a statistical post-processor capable of reducing errors and quantifying uncertainty in monthly streamflow predictions
The statistical post-processor is based on the Bayesian joint probability (BJP) modelling approach (Wang et al, 2009)
Summary
Streamflow predictions from a hydrological model can be used for wide range of applications including flood forecasting at short time scales to long-term assessments of water resources. Most post-processors produce probabilistic predictive distributions of streamflow (or river height) conditioned on model predictions and recent streamflow observations They generally assume linear dependence among the variates in a transformed normal space, and most use normal quantile transformation (NQT; Krzysztofowicz, 1997, 1999; Todini, 2008; Li et al, 2010) to normalize the variables. The BJP method was originally developed for forecasting seasonal streamflows in Australia (Wang et al, 2009) We apply it for bias correction, prediction updating and uncertainty quantification of monthly streamflow volumes generated from a monthly water balance model.
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