Hydrology Research (Mar 2024)
Randomized block quasi-Monte Carlo sampling for generalized likelihood uncertainty estimation
Abstract
Although hydrological model forecasts aid water management decisions, they normally have predictive uncertainties. Generalized likelihood uncertainty estimation (GLUE) is popular for constructing predictive uncertainty bounds (PUBs). GLUE is based on simple Monte Carlo sampling (SMCS), a technique known to be ineffective in establishing behavioural simulations. This study introduced randomized block quasi-Monte Carlo sampling (RBMC). In RBMC, each parameter's range is divided into a stipulated number of sub-blocks (Snb). Parameters' values are separately generated in each sub-block. Finally, the sub-blocks are shuffled while maintaining the sequence of generated values in each sub-block. When Snb is equal to the number of simulations, RBMC reduces to SMCS. Otherwise, each Snb leads to a separate RBMC configuration or sampling scheme. The number of RBMC-based behavioural solutions was often found to be greater than that of SMCS, in some cases, by up to 33.6%. The width of the 90% confidence interval on 95th percentile flow based on SMCS was often larger than those of RBMC, sometimes by up to 23.4%. PUBs were found to vary in widths among RBMC configurations, thereby revealing the influence of the choice of a sampling scheme. Thus, GLUE based on RBMC is recommended to take into account the said influence. HIGHLIGHTS GLUE uses simple Monte Carlo sampling (SMCS) which is ineffective in establishing behavioral simulations.; This study introduced randomized block quasi–Monte Carlo Sampling (RBMC) for GLUE and SMCS becomes one of the various RBMC configurations.; RBMC improved the number of retained solutions by up to 33.6% in some cases.; RBMC improved the width of 90% confidence interval on a flow event by up to 23.4%.; RBMC takes into account the influence of the choice of a sampling scheme as a sub-source of calibration uncertainty.;
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