Journal article
Propagating reliable estimates of hydrological forecast uncertainty to many lead times
JC Bennett, DE Robertson, QJ Wang, M Li, JM Perraud
Journal of Hydrology | Published : 2021
Abstract
We propose a revised version of the ERRIS (error reduction and representation in stages) error model capable of reliably propagating hydrological uncertainty to long lead times (>150 time steps). ERRIS employs four stages: a transformation to handle heteroscedasticity, a moving average bias-correction, an autoregressive model and two mixture Gaussian distributions. To propagate uncertainty through multiple lead times, ERRIS makes use of a technique termed ‘stochastic updating’. Ensemble spread at long lead times is partly controlled by the interplay of the autoregression coefficient ρ and the width of the error distribution. When ρ approaches 1 and the error distribution is wide, this causes..
View full abstractGrants
Awarded by Australian Research Council
Funding Acknowledgements
This research has been supported by the Water Information Research and Development Alliance (WIRADA) between the Bureau of Meteorology and CSIRO Land & Water, and by ARC linkage project LP170100922. We thank Prasantha Hapuarachchi and Jayaratne Liyanage (both Bureau of Meteorology) for supplying the catchment delineations and data. All data and model runs used in this paper are available from CSIRO's data access portal at https://data.csiro.au/co llections/collection/CIcsiro:51355v1. Matlab and C++ code used to generate simulations and forecasts, and to verify forecasts, are available on request; license conditions apply.