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Data assimilation (DA) methods have received increased attention as a means to accomplish uncertainty assessment and enhancement of prediction capability in various areas. Despite their potential, applicable software frameworks for probabilistic approaches and DA are still limited because most hydrological modeling frameworks are based on a deterministic approach. This paper presents a hydrological modeling framework for DA, namely MPI-OHyMoS. While adapting object-oriented features of the original OHyMoS, MPI-OHyMoS allows users to build a probabilistic hydrological model with DA. In this software framework, sequential DA based on particle filtering (PF) is available for any hydrological models considering various sources of uncertainty originating from input forcing, parameters, and observations. Ensemble simulations are parallelized by the message passing interface (MPI), which can take advantage of high-performance computing (HPC) systems. Structure and implementation processes of DA via MPI-OHyMoS are illustrated using a simple lumped model. We apply this software framework to uncertainty assessment of a distributed hydrological model in both synthetic and real experiment cases. In the synthetic experiment, dual state-parameter updating results in a reasonable estimation of parameters converging into the synthetic true. In the real experiment, dual updating also shows good conformity with the observed hydrograph, having reduced the uncertainty ranges of parameters. Deterministic modeling, based on parameters estimated via PF, shows good performance for extreme events, while dual updating via PF shows improved performance for all events.

DEVELOPMENT OF A HYDROLOGICAL MODELING FRAMEWORK FOR DATA ASSIMILATION WITH PARTICLE FILTERS