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A Novel Sequential Monte Carlo Algorithm for Parameter Estimation in Eco-Hydrological Models
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DOI：10.1029/2025wr040933.png)
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Abstract
En 中文
Bayesian inference offers a flexible framework for parameter estimation and uncertainty quantification in eco-hydrological models. However, simultaneously achieving robust posterior exploration and high computational efficiency for multimodal, high-dimensional, and computationally intensive targets remains challenging for the widely used Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. In this study, we developed the Parallel Adaptive Transition Particle Evolution Metropolis Sequential Monte Carlo (PATPEMS) algorithm, which is an adaptive and parallel SMC sampler for posterior distributions of model parameters in offline calibration. PATPEMS employs an adaptive sequence of intermediate distributions to control weight degeneracy and automatically select stages, a flexible scheduling of MCMC proposal kernels used to rejuvenate particles, together with reflection boundary handling to maintain particle diversity, and a particle-level parallelization scheme to exploit multicore architectures and reduce wall-clock time for computationally intensive models. Performance is assessed on four case studies: two synthetic targets probing multimodality and high-dimensional dependence, and a land surface model (LSM) with six parameters constrained by synthetic and real observations. Across all cases, PATPEMS provides close approximations to the target posteriors, judged against the analytic ground truth or reference solutions. For the LSM, parallelization yields substantial wall-clock speedups over the original non-parallel implementation. Compared with the original particle evolution Metropolis sequential Monte Carlo (PEM-SMC) algorithm, these results indicate that PATPEMS provides a more adaptive and parallel framework for robust Bayesian calibration of multimodal, correlated, and computationally demanding land surface and environmental models.
Keywords:
sequential Monte Carlo
Bayesian parameter calibration
adaptive intermediate distributions
differential evolution
parallel architecture
land surface model
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